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Call number: | 111810420X:ONLINE Show nearby items on shelf |
Title: | Reinforcement Learning and Approximate Dynamic Programming for Feedback Control |
Author(s): |
Lewis |
Date: | 2012 |
Publisher: | Wiley-IEEE Press |
Size: | 1 online resource (649 p.) |
ISBN: | 9781118104200 |
Series: | eBooks |
Series: | Wiley Online Library |
Series: | Wiley 2016 package purchase |
Keywords: | Electrical & Electronics Engineering |
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Call number: | 047166054X:ONLINE Show nearby items on shelf |
Title: | Handbook of Learning and Approximate Dynamic Programming |
Author(s): |
Si |
Date: | 2004 |
Publisher: | Wiley-IEEE Press |
Size: | 1 online resource (673 p.) |
ISBN: | 9780471660545 |
Series: | eBooks |
Series: | Wiley Online Library |
Series: | Wiley 2016 package purchase |
Keywords: | Electrical & Electronics Engineering |
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Call number: | 0471619779:ONLINE Show nearby items on shelf |
Title: | Markov Decision Processes: Discrete Stochastic Dynamic Programming |
Author(s): |
Puterman |
Date: | 1994 |
Publisher: | Wiley-Interscience |
Size: | 1 online resource (673 p.) |
ISBN: | 9780471619772 |
Series: | eBooks |
Series: | Wiley Online Library |
Series: | Wiley 2016 package purchase |
Keywords: | Statistics |
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Call number: | 0471161209:ONLINE Show nearby items on shelf |
Title: | Stochastic Dynamic Programming and the Control of Queueing Systems |
Author(s): |
Sennott |
Date: | 1998 |
Publisher: | Wiley-Interscience |
Size: | 1 online resource (355 p.) |
ISBN: | 9780471161202 |
Series: | eBooks |
Series: | Wiley Online Library |
Series: | Wiley 2016 package purchase |
Keywords: | Statistics |
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Call number: | 047060445X:ONLINE Show nearby items on shelf |
Title: | Approximate Dynamic Programming: Solving the Curses of Dimensionality, Second Edition |
Author(s): |
Powell |
Date: | 2011 |
Publisher: | Wiley |
Size: | 1 online resource (657 p.) |
ISBN: | 9780470604458 |
Series: | eBooks |
Series: | Wiley Online Library |
Series: | Wiley 2016 package purchase |
Keywords: | Statistics |
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Call number: | T57.83.B475::2007 Show nearby items on shelf |
Title: | Dynamic programming and optimal control, Volume 1 |
Author(s): |
Dimitri P. Bertsekas |
Date: | 2005 |
Edition: | 3rd ed. |
Publisher: | Athena Scientific |
Size: | 543 p |
ISBN: | 9781886529267 |
Series: | Athena Scientific optimization and computation series |
Keywords: | Dynamic programming. , Control theory. , Mathematical optimization. |
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Call number: | T57.83.B475.2::2007 Show nearby items on shelf |
Title: | Dynamic programming and optimal control, Volume 2, Approximate dynamic programming |
Author(s): |
Dimitri P. Bertsekas |
Date: | 2007 |
Edition: | 4th ed. |
Publisher: | Athena Scientific |
Size: | 694 p |
Note: | 2nd volume of 2 volumes |
ISBN: | 9781886529441 |
Series: | Athena Scientific optimization and computation series |
Keywords: | Dynamic programming. , Control theory , Mathematical optimization. |
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Call number: | SPRINGER-2016-9783319411927:ONLINE Show nearby items on shelf |
Title: | Search and Optimization by Metaheuristics Techniques and Algorithms Inspired by Nature |
Author(s): |
Ke-Lin Du |
Date: | 2016 |
Size: | 1 online resource (40 p.) |
Note: | 10.1007/978-3-319-41192-7 |
Contents: | Preface -- Introduction -- Simulated Annealing -- Optimization by Recurrent Neural Networks -- Genetic Algorithms and Genetic Programming -- Evolutionary Strategies -- Differential Evolution -- Estimation of Distribution Algorithms -- Mimetic Algor ithms -- Topics in EAs -- Particle Swarm Optimization -- Artificial Immune Systems -- Ant Colony Optimization -- Tabu Search and Scatter Search -- Bee Metaheuristics -- Harmony Search -- Biomolecular Computing -- Quantum Computing -- Other Heuristics-Insp ired Optimization Methods -- Dynamic, Multimodal, and Constraint-Satisfaction Optimizations -- Multiobjective Optimization -- Appendix 1: Discrete Benchmark Functions -- Appendix 2: Test Functions -- Index |
ISBN: | 9783319411927 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Keywords: | Mathematics , Computer simulation , Algorithms , Computer mathematics , Mathematical optimization , Computational intelligence , Mathematics , Computational Science and Engineering , Algorithms , Optimization , Simulation and Modeling , Computational Intelligence |
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Call number: | SPRINGER-2016-9783319255897:ONLINE Show nearby items on shelf |
Title: | Stochastic Analysis for Finance with Simulations |
Author(s): |
Geon Ho Choe |
Date: | 2016 |
Size: | 1 online resource (107 p.) |
Note: | 10.1007/978-3-319-25589-7 |
Contents: | Preface -- Acknowledgements -- List of Figures -- List of Tables -- List of Simulations -- Fundamental Concepts -- Financial Derivatives -- The Lebesgue Integral -- Basic Probability Theory -- Conditional Expectation -- Stochastic Processes -- Brown ian Motion -- Girsanov's Theorem -- The Reflection Principle of Brownian Motion -- The Ito Integral -- The Ito Formula -- Stochastic Differential Equations -- The Feynmann-Kac Theorem -- The Binomial Tree Method for Option Pricing -- The Black-Scholes-Mer ton Differential Equation -- The Martingale Method -- Pricing of Vanilla Options -- Pricing of Exotic Options -- American Options -- The Capital Asset Pricing Model -- Dynamic Programming -- Bond Pricing -- Interest Rate Models -- Numeraires -- Numerical Estimation of Volatility -- Time Series -- Random Numbers -- The Monte Carlo Method for Option Pricing -- Numerical Solution of the Black-Scholes-Merton Equation -- Numerical Solution of Stochastic Differential Equations. Appendices -- |
Solutions for Selected Problems -- Glossary -- References -- Index. | |
ISBN: | 9783319255897 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Keywords: | Mathematics , Economics, Mathematical , Mathematics , Mathematics, general , Quantitative Finance |
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Call number: | SPRINGER-2014-9783658044763:ONLINE Show nearby items on shelf |
Title: | A Direct Method for Parabolic PDE Constrained Optimization Problems [electronic resource] |
Author(s): |
Andreas Potschka |
Date: | 2014 |
Publisher: | Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum |
Size: | 1 online resource |
Note: | Andreas Potschka discusses a direct multiple shooting method for dynamic optimization problems constrained by nonlinear, possibly time-periodic, parabolic partial differential equations. In contrast to indirect methods, thisapproach automatically com putes adjoint derivatives without requiring the user to formulate adjoint equations, which can be time-consuming and error-prone. The author describes and analyzes in detail a globalized inexact SequentialQuadratic Programming method that exploits the mat hematical structures of this approach and problem class for fast numerical performance. The book features applications, including results for a real-world chemical engineeringseparation problem. Contents Parabolic PDE Constrained Optimization Problems Two-Grid Newton-Picard Inexact SQP Structure Exploiting Solution of QPs Applications and Numerical Results Target Groups Researchers and students in the fields of mathematics, information systems, and scientific computing Users with PDE constrainedo ptimization problems, in particular in (bio-)chemical engineering The Author Dr. Andreas Potschka is a postdoctoral researcher in the Simulation and Optimization group of Prof. Dr. Dres. h.c. Hans Georg Bock at theInterdisciplinary Center for Scientific Computing, Heidelberg University. He is the head of the research group Model-Based Optimizing Control |
Contents: | Parabolic PDE Constrained Optimization Problems |
Two | |
Grid Newton | |
Picard Inexact SQP | |
Structure Exploiting Solution of QPs | |
Applications and Numerical Results | |
ISBN: | 9783658044763 |
Series: | eBooks |
Series: | SpringerLink |
Series: | Advances in Numerical Mathematics, 1616-2994 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Biochemical engineering , Differential equations, partial , Mathematical optimization |
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Call number: | SPRINGER-2014-9783319050140:ONLINE Show nearby items on shelf |
Title: | Mathematical and Statistical Methods for Actuarial Sciences and Finance [electronic resource] |
Author(s): |
Cira Perna Marilena Sibillo |
Date: | 2014 |
Publisher: | Cham : Springer International Publishing : Imprint: Springer |
Size: | 1 online resource |
Note: | The interaction between mathematicians and statisticians working in the actuarial and financial fields is producing numerous meaningful scientific results. This volume, comprising a series of four-page papers, gathers new ideasrelating to mathematica l and statistical methods in the actuarial sciences and finance. The book covers a variety of topics of interest from both theoretical and applied perspectives, including: actuarial models alternative testingapproaches behavioral finance clustering techni ques coherent and non-coherent risk measures credit-scoring approaches data envelopment analysis dynamic stochastic programming financial contagion models financial ratios |
Contents: | 1 I. Albarrn, P. Alonso, A.Arribas |
Gil and A. Gran: Can personal dependency paths help to estimate life expectancy free of dependency? | |
2 A. Amendola and V. Candila: Evaluation of volatility forecasts in a VaR framework | |
3 A. Amendola and M. Restaino: Optimal cut | |
off points for multiple causes of business failure models | |
4 R. Baragona, F. Battaglia and D. Cucina: Maximum empirical likelihood inference for outliers in autoregressive time series | |
5A. Basso and S. Funari: The role of fund size and returns to scale in the performance of mutual funds | |
6 M. Biancardi and G. Villani: A ro | |
ISBN: | 9783319050140 |
Series: | eBooks |
Series: | SpringerLink |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Finance , Mathematical statistics |
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Call number: | SPRINGER-2014-9783319024998:ONLINE Show nearby items on shelf |
Title: | Mathematical and Statistical Methods for Actuarial Sciences and Finance [electronic resource] |
Author(s): |
Marco Corazza Claudio Pizzi |
Date: | 2014 |
Publisher: | Cham : Springer International Publishing : Imprint: Springer |
Size: | 1 online resource |
Note: | The interaction between mathematicians and statisticians has been shown to be an eective approach for dealing with actuarial, insurance and nancial problems, both from an academic perspective and from an operative one. Thecollection of original paper s presented in this volume pursues precisely this purpose. It covers a wide variety of subjects in actuarial, insurance and nance elds, all treated in the light of the successful cooperation betweenthe above two quantitative approaches. The papers publish ed in this volume present theoretical and methodological contributions and their applications to real contexts. With respect to the theoretical and methodological contributions,some of the considered areas of investigation are: actuarial models alternativ e testing approaches behavioral nance clustering techniques coherent and non-coherent risk measures credit scoring approaches data envelopmentanalysis dynamic stochastic programming nancial contagion models nancial ratios intelligent nancial trading syste ms mixture normality approaches Monte Carlo-based methods multicriteria methods nonlinear parameterestimation techniques nonlinear threshold models particle swarm optimization performance measures portfolio optimization pricing methods for structured and non-structured derivatives risk management skewed distribution analysis |
Contents: | Weak form efficiency of selected European stock markets: alternative testing approaches (G. Albano, M. La Rocca, C. Perna) |
An empirical comparison of variable selection methods in competing risks model (A. Amendola, M. Restaino, L. Sensini) | |
A comparison between different numerical schemes for the valuation of unit | |
linked contracts embedding a surrender option (A.R. Bacinello, P. Millossovich, A. Montealegre) | |
Dynamic tracking error with shortfall control using stochastic programming (D. Barro, E. Canestrelli) | |
Firms volatility risk under microstructure noise (F. Barsotti, S. Sanfe | |
ISBN: | 9783319024998 |
Series: | eBooks |
Series: | SpringerLink |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Finance , Economics Statistics |
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Call number: | SPRINGER-2014-9781493909957:ONLINE Show nearby items on shelf |
Title: | Stochastic Optimization in Insurance [electronic resource] : A Dynamic Programming Approach |
Author(s): |
Pablo Azcue Nora Muler |
Date: | 2014 |
Publisher: | New York, NY : Springer New York : Imprint: Springer |
Size: | 1 online resource |
Note: | The main purpose of the book is to show how a viscosity approach can be used to tackle control problems in insurance. The problems covered are the maximization of survival probability as well as the maximization of dividends inthe classical collectiv e risk model. The authors consider the possibility of controlling the risk process by reinsurance as well as by investments. They show that optimal value functions are characterized as either the unique or thesmallest viscosity solution of the associated Hamilton-Jacobi-Bellman equation they also study the structure of the optimal strategies and show how to find them. The viscosity approach was widely used in control problems related tomathematical finance but until quite recently it was not used to solve control problems related to actuarial mathematical science. This book is designed to familiarize the reader on how to use this approach. The intended audience isgraduate students as well as researchers in this area |
Contents: | Stability Criteria for Insurance Companies |
Reinsurance and Investment | |
Viscosity Solutions | |
Characterization of Value Functions | |
Optimal Strategies | |
Numerical Examples | |
References | |
Appendix A. Probability Theory and Stochastic Processes | |
Index | |
ISBN: | 9781493909957 |
Series: | eBooks |
Series: | SpringerLink |
Series: | SpringerBriefs in Quantitative Finance, 2192-7006 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Finance , Distribution (Probability theory) |
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Call number: | SPRINGER-2013-9783642324604:ONLINE Show nearby items on shelf |
Title: | Traffic Flow Dynamics [electronic resource] : Data, Models and Simulation |
Author(s): |
Martin Treiber Arne Kesting |
Date: | 2013 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This textbook provides a comprehensive and instructive coverage of vehicular traffic flow dynamics and modeling. It makes this fascinating interdisciplinary topic, which to date was only documented in parts by specializedmonographs, accessible to a b road readership. Numerous figures and problems with solutions help the reader to quickly understand and practice the presented concepts. This book is targeted at students of physics and traffic engineeringand, more generally, also at students and professi onals in computer science, mathematics, and interdisciplinary topics. It also offers material for project work in programming and simulation at college and university level. The mainpart, after presenting different categories of traffic data, is devoted t o a mathematical description of the dynamics of traffic flow, covering macroscopic models which describe traffic in terms of density, as well as microscopicmany-particle models in which each particle corresponds to a vehicle and its driver.Focus chapters on traffic instabilities and model calibration/validation present these topics in a novel and systematic way.Finally, thetheoretical framework is shown at work in selected applications such as traffic-state and travel-time estimation, intelligent transpor tation systems, traffic operations management, and a detailed physics-based model for fuelconsumption and emissions |
Note: | Springer eBooks |
Contents: | Introduction |
Part I Traffic Data: Trajectroy and Floating | |
Car Data | |
Cross | |
Sectional Data | |
Representations of Cross | |
Sectional Data | |
Spatiotemporal Reconstruction of the Traffic State | |
Part II Traffic Flow Modeling: General Aspects | |
Continuity Equation | |
The Lighthill | |
Whitham | |
Richards Model | |
Macroscopic Models with Dynamic Velocity | |
Elementary Car | |
Following Models | |
Car | |
Following Models based on Driving Strategies | |
Modeling Human Aspects of Driving Behavior | |
Cellular Automata | |
Lane | |
Changing and other Discrete | |
Choice Situations | |
Stability Analysis | |
Calibration and Val | |
ISBN: | 9783642324604 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Physics and Astronomy (Springer-11651) |
Keywords: | Mathematical optimization , Engineering , Operations research |
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Call number: | SPRINGER-2013-9783319026909:ONLINE Show nearby items on shelf |
Title: | Advances in Dynamic Games [electronic resource] : Theory, Applications, and Numerical Methods |
Author(s): |
Vlastimil Kivan Georges Zaccour |
Date: | 2013 |
Publisher: | Cham : Springer International Publishing : Imprint: Birkhuser |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This contributed volume focuses on aspects of dynamic game theory including differential games, evolutionary games, and stochastic games. It covers theoretical developments, algorithmic methods, and applications to fields asvaried as mathematical bio logy, environmental management, economics, engineering, guidance and control, and social interaction. It will be of interest to an interdisciplinary audience of researchers, practitioners, and advancedgraduate students. Advances in Dynamic Games presents state-of-the-art research that serves as a testament to the vitality and growth of the field of dynamic games and their applications. Its contributions, written by experts in theirrespective disciplines, are outgrowths of presentations originally given at the 15th International Symposium of Dynamic Games and Applications held July 1922, 2012, in Byice, Czech Republic |
Note: | Springer eBooks |
Contents: | Part I Dynamic Games: Theory and Computation |
1 Relative Value Iteration for Stochastic Differential Games | |
2 OPTGAME3: A Dynamic Game Solver and an Economic Example | |
3 Dynamic Programming Approach to Aircraft Control in a Windshear | |
4 Existence of Optimal Controls for a Bi | |
Level Optimal Control Problem | |
5 Static Linear | |
Quadratic Gaussian Games | |
6 Interior Convergence Under Payoff Monotone Selections and Proper Equilibrium: Application to Equilibrium Selection | |
Part II Dynamic Games: Applications | |
7 Should a Retailer Support a Quality Improvements Strategy? | |
8 A Large Populat | |
ISBN: | 9783319026909 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Annals of the International Society of Dynamic Games : v13 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Engineering mathematics , Economics, Mathematical |
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Call number: | SPRINGER-2013-9783319010595:ONLINE Show nearby items on shelf |
Title: | DiscreteTime Stochastic Control and Dynamic Potential Games [electronic resource] : The EulerEquation Approach |
Author(s): |
David Gonzlez-Snchez Onsimo Hernndez-Lerma |
Date: | 2013 |
Publisher: | Cham : Springer International Publishing : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | There are several techniques to study noncooperative dynamic games, suchas dynamic programming and the maximum principle (also called the Lagrangemethod). It turns out, however, that one way to characterize dynamicpotentialgames requires to analyze i nverse optimal control problems, and it is here wherethe Euler equation approach comes in because it is particularly wellsuited tosolve inverse problems.Despite the importance of dynamicpotential games, there is no systematicstudy about them. Thismonograp h isthe firstattempt to provide a systematic, selfcontained presentation of stochastic dynamicpotential games |
Note: | Springer eBooks |
Contents: | Introduction and summary |
Direct problem: the Euler equation approach | |
The inverse optimal control problem | |
Dynamic games | |
Conclusion | |
References | |
Index | |
ISBN: | 9783319010595 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | SpringerBriefs in Mathematics, 2191-8198 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Systems theory , Distribution (Probability theory) |
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Call number: | SPRINGER-2013-9781461474289:ONLINE Show nearby items on shelf |
Title: | Statistical Methods for Dynamic Treatment Regimes [electronic resource] : Reinforcement Learning, Causal Inference, and Personalized Medicine |
Author(s): |
Bibhas Chakraborty Erica E.M Moodie |
Date: | 2013 |
Publisher: | New York, NY : Springer New York : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. Thisvolume demonstrates thes e methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medicalparadigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, andtechnical reports with the goal of orienting researchers to the fiel d. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementarycalculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where codedoes not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowled ge of statistical programming could implement the methods from scratch. This will be an important volume for a widerange of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also findmaterial in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies |
Note: | Springer eBooks |
Contents: | Introduction |
The Data: Observational Studies and Sequentially Randomized Trials | |
Statistical Reinforcement Learning | |
Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes | |
Estimation of Optimal DTRs by Directly Modeling Regimes | |
G | |
computation: Parametric Estimation of Optimal DTRs | |
Estimation DTRs for Alternative Outcome Types | |
Inference and Non | |
regularity | |
Additional Considerations and Final Thoughts | |
Glossary | |
Index | |
References | |
ISBN: | 9781461474289 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Statistics for Biology and Health, 1431-8776 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Medical records Data processing |
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Call number: | SPRINGER-2013-9781461461142:ONLINE Show nearby items on shelf |
Title: | Sequential Experimentation in Clinical Trials [electronic resource] : Design and Analysis |
Author(s): |
Jay Bartroff Tze Leung Lai Mei-Chiung Shih |
Date: | 2013 |
Publisher: | New York, NY : Springer New York : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book presents an integrated methodology for sequential experimentation in clinical trials. The methodology allows sequential learning during the course of a trial to improve the efficiency of the trial design, which oftenlacks adequate informati on at the planning stage. Adaptation via sequential learning of unknown parameters is a central idea not only in adaptive designs of confirmatory clinical trials but also in the theory of optimal nonlinearexperimental design, which the book covers as intr oductory material. Other introductory topics for which the book provides preparatory background include sequential testing theory, dynamic programming and stochastic optimization,survival analysis and resampling methods. In this way, the book gives a self -contained and thorough treatment of group sequential and adaptive designs, time-sequential trials with failure-time endpoints, and statistical inference atthe conclusion of these trials. The book can be used for graduate courses in sequential analysis, c linical trials, and biostatistics, and also for short courses on clinical trials at professional meetings. Each chapter ends withsupplements for the reader to explore related concepts and methods, and problems which can be used for exercises in graduate c ourses. Jay Bartroff is Associate Professor of Mathematics at the University of Southern California where heis a member of the Laboratory of Applied Pharmacokinetics at the USC Keck School of Medicine. He is a leading expert on group sequential and multis tage adaptive statistical procedures and their applications to clinical trial designs,and he is a sought-after consultant in academia and industry. Tze Leung Lai is Professor of Statistics, and by courtesy, of Health Research and Policy and of the Institu te of Computational and Mathematical Engineering at StanfordUniversity, where he is the Director of the Financial and Risk Modeling Institute and Co-director of the Biostatistics Core at the Stanford Ca |
Note: | Springer eBooks |
Contents: | Introduction |
Nonlinear Regression, Experimental Design, and Phase I Clinical Trials | |
Sequential Testing Theory and Stochastic Optimization over Time | |
Group Sequential Design of Phase II and III Trials | |
Sequential Methods for Vaccine Safety Evaluation and Surveillance in Public Health | |
Time | |
Sequential Design of Clinical Trials with Failure | |
Time Endpoints | |
Confidence Intervals and p | |
Values | |
Adaptive Design of Confirmatory Trials | |
References | |
ISBN: | 9781461461142 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Series in Statistics, 0172-7397 : v298 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Mathematical statistics |
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Call number: | SPRINGER-2013-9781461442868:ONLINE Show nearby items on shelf |
Title: | Optimal Stochastic Control, Stochastic Target Problems, and Backward SDE [electronic resource] |
Author(s): |
Nizar Touzi |
Date: | 2013 |
Publisher: | New York, NY : Springer New York : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book collects some recent developments in stochastic control theory with applications to financial mathematics. In the first part of the volume, standard stochastic control problems are addressed from the viewpoint of therecently developed weak dynamic programming principle. A special emphasis is put on regularity issues and, in particular, on the behavior of the value function near the boundary. Then a quick review of the main tools from viscositysolutions allowing one to overcome all regularit y problems is provided. The second part is devoted to the class of stochastic target problems, which extends in a nontrivial way the standard stochastic control problems. Here thetheory of viscosity solutions plays a crucial role in the derivation of the dynamic programming equation as the infinitesimal counterpart of the corresponding geometric dynamic programming equation. The various developments of thistheory have been stimulated by applications in finance and by relevant connections with geometric f lows namely, the second order extension was motivated by illiquidity modeling, and the controlled loss version was introduced followingthe problem of quantile hedging. The third part presents an overview of backward stochastic differential equations and their extensions to the quadratic case. Backward stochastic differential equations are intimately related to thestochastic version of Pontryagins maximum principle and can be viewed as a strong version of stochastic target problems in the non-Markov conte xt. The main applications to the hedging problem under market imperfections, the optimalinvestment problem in the exponential or power expected utility framework, and some recent developments in the context of a Nash equilibrium model for interacting inve stors, are presented. The book concludes with a review of thenumerical approximation techniques for nonlinear partial differential equations based on monotonic schemes methods in the theory o |
Note: | Springer eBooks |
Contents: | Preface |
1. Conditional Expectation and Linear Parabolic PDEs | |
2. Stochastic Control and Dynamic Programming | |
3. Optimal Stopping and Dynamic Programming | |
4. Solving Control Problems by Verification | |
5. Introduction to Viscosity Solutions | |
6. Dynamic Programming Equation in the Viscosity Sense | |
7. Stochastic Target Problems | |
8. Second Order Stochastic Target Problems | |
9. Backward SDEs and Stochastic Control | |
10. Quadratic Backward SDEs | |
11. Probabilistic Numerical Methods for Nonlinear PDEs | |
12. Introduction to Finite Differences Methods | |
References | |
ISBN: | 9781461442868 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Fields Institute Monographs, 1069-5273 : v29 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Differential equations, partial , Finance , Mathematical optimization , Distribution (Probability theory) |
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Call number: | SPRINGER-2012-9789400722477:ONLINE Show nearby items on shelf |
Title: | Convexity and Optimization in Banach Spaces [electronic resource] |
Author(s): |
Viorel Barbu Teodor Precupanu |
Date: | 2012 |
Edition: | 4th ed. 2012 |
Publisher: | Dordrecht : Springer Netherlands : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | An updated and revised edition of the 1986 title Convexity and Optimization in Banach Spaces, this book provides a self-contained presentation of basic results of the theory of convex sets and functions in infinite-dimensionalspaces. The main emphasi s is on applications to convex optimization and convex optimal control problems in Banach spaces. A distinctive feature is a strong emphasis on the connection between theory and application. This edition hasbeen updated to include new results pertaining t o advanced concepts of subdifferential for convex functions and new duality results in convex programming. The last chapter, concerned with convex control problems, has been rewritten andcompleted with new research concerning boundary control systems, the dynamic programming equations in optimal control theory and periodic optimal control problems. Finally, the structure of the book has been modified to highlight themost recent progression in the field including fundamental results on the theory of infini te-dimensional convex analysis and includes helpful bibliographical notes at the end of each chapter |
Note: | Springer eBooks |
Contents: | Fundamentals of Functional Analysis |
Convex Functions | |
Convex Programming | |
Convex Control Problems in Banach Spaces | |
ISBN: | 9789400722477 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Monographs in Mathematics, 1439-7382 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Mathematical optimization |
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Call number: | SPRINGER-2012-9783034804363:ONLINE Show nearby items on shelf |
Title: | Mathematical Optimization of Water Networks [electronic resource] |
Author(s): |
Alexander Martin Kathrin Klamroth Jens Lang Gnter Leugering Antonio Morsi Martin Oberlack Manfred Ostrowski Roland Rosen |
Date: | 2012 |
Publisher: | Basel : Springer Basel : Imprint: Birkhuser |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Water supply- and drainage systems and mixed water channel systems are networks whose high dynamic is determined and/or affected by consumer habits on drinking water on the one hand and by climate conditions, in particularrainfall, on the other hand. According to their size, water networks consist of hundreds or thousands of system elements. Moreover, different types of decisions (continuous and discrete) have to be taken in the water management. Thenetworks have to be optimized in terms of topology and operation by targeting a variety of criteria. Criteria may for example be economic, social or ecological ones and may compete with each other. The development of complex modelsystems and their use for deriving optimal decisions in water management is taking place at a rapid pace. Simulation and optimization methods originating in Operations Research have been used for several decades usually with verylimited direct cooperation with applied mathematics. The research presented here aims at bridging this gap, thereby opening up space for synergies and innovation. It is directly applicable for relevant practical problems and has beencarried out in cooperation with utility and dumping companies, infrastructure providers and planning offices. A close and di rect connection to the practice of water management has been established by involving application-orientedknow-how from the field of civil engineering. On the mathematical side all necessary disciplines were involved, including mixed-integer optimization, multi-objective and facility location optimization, numerics for cross-linked dynamictransportation systems and optimization as well as control of hybrid systems. Most of the presented research has been supported by the joint project Discret-continuous o ptimization of dynamic water systems of the federalministry of education and research (BMBF) |
Note: | Springer eBooks |
Contents: | Part I Optimization of Water Supply Networks |
Modelling and Numerical Simulation of Pipe Flow Problems in Water Supply Systems | |
Simulation and Continuous Optimization | |
Mixed Integer Optimizationof Water Supply Networks | |
Nonlinear and Mixed Integer Linear Programming | |
Part II Optimal Control of Sewer Networks | |
Optimal Control of Sewer Networks Problem Description | |
Modelling of Channel Flows with Transition Interface Separating Free Surface and Pressurized Channel Flows | |
Optimal Control of Sewer Networks Engineers View | |
Real | |
Time Control of Urban Drainage Systems | |
Performance | |
ISBN: | 9783034804363 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | International Series of Numerical Mathematics : v162 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computer science Mathematics , Mathematical optimization |
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Call number: | SPRINGER-2012-9781461434337:ONLINE Show nearby items on shelf |
Title: | Topics in Numerical Methods for Finance [electronic resource] |
Author(s): |
Mark Cummins Finbarr Murphy John J.H Miller |
Date: | 2012 |
Publisher: | Boston, MA : Springer US : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Presenting state-of-the-art methods in the area, the book begins with a presentation of weak discrete time approximations of jump-diffusion stochastic differential equations for derivatives pricing and risk measurement. Using amoving least squares re construction, a numerical approach is then developed that allows for the construction of arbitrage-free surfaces. Free boundary problems are considered next, with particular focus on stochastic impulse controlproblems that arise when the cost of control i ncludes a fixed cost, common in financial applications. The text proceeds with the development of a fear index based on equity option surfaces, allowing for the measurement of overall fearlevels in the market. The problem of American option pricing is con sidered next, applying simulation methods combined with regression techniques and discussing convergence properties. Changing focus to integral transform methods, avariety of option pricing problems are considered. The COS method is practically applied fo r the pricing of options under uncertain volatility, a method developed by the authors that relies on the dynamic programming principle andFourier cosine series expansions. Efficient approximation methods are next developed for the application of the fast Fourier transform for option pricing under multifactor affine models with stochastic volatility and jumps. Followingthis, fast and accurate pricing techniques are showcased for the pricing of credit derivative contracts with discrete monitoring based on the Wiener-Hopf factorisation. With an energy theme, a recombining pentanomial lattice isdeveloped for the pricing of gas swing contracts under regime switching dynamics. The book concludes with a linear and nonlinear review of the arbitrage-free parity t heory for the CDS and bond markets |
Note: | Springer eBooks |
Contents: | On Weak Predictor |
Corrector Schemes for Jump | |
Diffusion Processes in Finance | |
Moving Least Squares for Arbitrage | |
Free Price and Volatility Surfaces | |
Solving Impulse Control Problems with Control Delays | |
FIX: The Fear Index ? Measuring Market Fear | |
American Option Pricing using Simulation and Regression: Numerical Convergence Results | |
The COS Method for Pricing Options under Uncertain Volatility | |
Fast Fourier Transform Option Pricing: Efficient Approximation Methods under Multi | |
Factor Stochastic Volatility and Jumps | |
Pricing Credit Derivatives in a Wiener | |
Hopf Framework | |
The Eval | |
ISBN: | 9781461434337 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Proceedings in Mathematics & Statistics, 2194-1009 : v19 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Finance , Computer science Mathematics |
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Call number: | SPRINGER-2012-9781461432265:ONLINE Show nearby items on shelf |
Title: | Pyomo Optimization Modeling in Python [electronic resource] |
Author(s): |
William E Hart Carl Laird Jean-Paul Watson David L Woodruff |
Date: | 2012 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book provides a complete and comprehensive guide to Pyomo(Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, andpractitioners. Modeling is a fundamental process in many aspects of scientific research, engineering, and business. This text beautifully illustrates the breadth of the modeling capabilities that are supported by this new software andits handling of complex real-world applications . Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (AlgebraicModeling Language) tools.Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language.Python is a powerful and dynamic programming language that has a very clear, readable syntaxand intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parame ters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can beused from a command-line interface and within Python's interactive command environment, which makes it easy to create P yomo models, apply a variety of optimizers, and examine solutions. The text begins with a tutorial on simplelinear and integer programming models. Information needed to install and get started with the software is also provided. A detailed reference of P yomo's modeling components is illustrated with extensive examples, including a discussionof how to load data from sources like spreadsheets and databases. The final chapters cover advanced topics such as nonlinear models, stochastic models, and scripting examples |
Note: | Springer eBooks |
Contents: | Preface |
1. Introduction | |
2. Pyomo Modeling Strategies | |
3. Model Components: Variables, Objectives and Constraints | |
4. Model Components: Sets and Parameters | |
5. Mischellaneous Model Components and Utility Functions | |
6. Initializing Abstract Models with Data Command Files | |
7. The Pyomo Command | |
Line Interface | |
8. Nonlinear Programming with Pyomo | |
9. Stochastic Programming Extensions | |
10. Scripting and Algorithm Development | |
A. Installing Coopr | |
B. A Brief Python Tutorial | |
C. Pyomo and Coopr: The Bigger Picture | |
Index | |
ISBN: | 9781461432265 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Optimization and Its Applications, 1931-6828 : v67 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computer science , Computer simulation , Computer science Mathematics , Computer software , Mathematical optimization |
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Call number: | SPRINGER-2012-9780817683375:ONLINE Show nearby items on shelf |
Title: | Optimization, Control, and Applications of Stochastic Systems [electronic resource] : In Honor of Onsimo Hernndez-Lerma |
Author(s): |
Daniel Hernndez-Hernndez J. Adolfo Minjrez-Sosa |
Date: | 2012 |
Publisher: | Boston : Birkhuser Boston : Imprint: Birkhuser |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Compiled in honor of Onsimo Hernndez-Lerma, this volume offers a broad presentation of the main concepts, techniques, and methodologies in the fields of optimization and control of stochastic systems. At the same time, thebook provides an overview of their wide-ranging applications and theoretical developments. These topics include various aspects of dynamic programming, discounted and average optimality criteria for discrete- and continuous-timecontrol processes, approximation algorithms, optimal sto pping, and games. The work comprises 18 carefully selected papers written by experts in their respective fields, and explores five major themes: * discrete-time Markov controlprocesses * several optimality criteria * applications in inventory systems and finance * stochastic optimal control problems for diffusion * optimization. This book will be a valuable resource for all practitioners, researchers,and professionals in applied mathematics and operations research who work in the areas of stochastic contr ol, mathematical finance, queueing theory,and inventory systems. It may also serve as a supplemental text for graduate coursesin optimal control and its applications |
Note: | Springer eBooks |
Contents: | 1 On the Policy Iteration Algorithm for Non |
degenerate Controlled Diffusions under the Ergodic Criterion | |
2 Discrete | |
Time Inventory Problems with Lead Time and Order | |
Time Constraint | |
3 Sample | |
Path Optimality in Average Markov Decision Chains | |
4 Approximation of Infinite Horizon Discounted Cost Markov | |
5 Reduction of Discounted Continuous | |
Time MDPs with Unbounded | |
6 Continuous | |
Time Controlled Jump Markov Processes on the Finite | |
7 Existence and Uniqueness of Solutions of SPDEs in Infinite Dimensions | |
8 A Constrained Optimization Problem with Applications to Constrained | |
9 Optima | |
ISBN: | 9780817683375 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Systems & Control: Foundations & Applications |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Systems theory , Mathematical optimization , Engineering mathematics |
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Call number: | SPRINGER-2012-9780817681524:ONLINE Show nearby items on shelf |
Title: | The Robust Maximum Principle [electronic resource] Theory and Applications |
Author(s): |
Vladimir G Boltyanski Alexander S Poznyak |
Date: | 2012 |
Publisher: | Boston : Birkhuser Boston |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Both refining and extending previous publications by the authors, the material in thismonograph has been class-tested in mathematical institutions throughout the world. Covering some of the key areas of optimal control theory(OCT)a rapidly expanding field that has developed to analyze the optimal behavior of a constrained process over timethe authors use new methods to set out a version of OCTs more refinedmaximum principle designed tosolve the problem of constructing optimal control strategies for u ncertain systems where some parameters are unknown. Referred to as a min-max problem, this type of difficulty occurs frequently when dealing with finite uncertainsets. The text begins with a standalone section that reviews classical optimal control theory ,coveringthe principal topics of themaximum principle and dynamic programming and considering the important sub-problems of linearquadratic optimal control and time optimization. Moving on to examine the tent method in detail, the book thenpresents its co re material, which is a more robust maximum principle for both deterministic and stochastic systems.Theresults obtainedhave applicationsin production planning, reinsurance-dividend management, multi-model sliding mode control, and multi-model differential games. Key features and topics include: * A version of the tent method inBanach spaces * How to apply the tent method to a generalization of the Kuhn-Tucker Theorem as well as the Lagrange Principle for infinite-dimensional spaces * A detailed considerat ion of the min-max linear quadratic (LQ) controlproblem * The application of obtained results from dynamic programming derivations to multi-model sliding mode control and multi-model differential games * Two examples, dealing with production planning and reinsurance-dividendmanagement, that illustrate the use of the robust maximum principle in stochastic systems Using powerful new tools in optimal control theory, T |
Note: | Springer eBooks |
Contents: | Preface |
Introduction | |
I Topics of Classical Optimal Control | |
1 Maximum Principle | |
2 Dynamic Programming | |
3 Linear Quadratic Optimal Control | |
4 Time | |
Optimization Problem | |
II Tent Method | |
5 Tent Method in Finite Dimensional Spaces | |
6 Extrenal Problems in Banach Space | |
III Robust Maximum Principle for Deterministic Systems | |
7 Finite Collection of Dynamic Systems | |
8 Multi | |
Model Bolza and LQ | |
Problem | |
9 Linear Multi | |
Model Time | |
Optimization | |
10 A Measured Space as Uncertainty Set | |
11 Dynamic Programming for Robust Optimization | |
12 Min | |
Max Sliding Mode Control | |
13 Mu | |
ISBN: | 9780817681524 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Systems & Control: Foundations & Applications |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Systems theory , Mathematical optimization , Engineering mathematics , Vibration |
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Call number: | SPRINGER-2011-9781461402374:ONLINE Show nearby items on shelf |
Title: | Introduction to Stochastic Programming [electronic resource] |
Author(s): |
John R Birge Franois Louveaux |
Date: | 2011 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research,mathematics, and probability. A t the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a firstcourse in stochastic programming suitable for student s with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Itsprime goal is to help students develop an intuition on how to model uncerta inty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material on methods and examples includin g several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter onrelationships to other methods including approximate dynamic programming, robust optimization and online methods. T he book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers andpractitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition: The discussion on modeling issues, the large number of examples used to illustrate the material, and thebreadth of the coverage make'Introduction to Stochastic Programming' an ideal textbook for the area. (Interfaces, 1998) |
Note: | Springer eBooks |
Contents: | Introduction and Examples |
Uncertainty and Modeling Issues | |
Basic Properties and Theory | |
The Value of Information and the Stochastic Solution | |
Two | |
Stage Recourse Problems | |
Multistage Stochastic Programs | |
Stochastic Integer Programs | |
Evaluating and Approximating Expectations | |
Monte Carlo Methods | |
Multistage Approximations | |
Sample Distribution Functions | |
References | |
ISBN: | 9781461402374 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Series in Operations Research and Financial Engineering, 1431-8598 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Mathematical optimization , Mathematical statistics |
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Call number: | SPRINGER-2010-9783642038228:ONLINE Show nearby items on shelf |
Title: | CATBox [electronic resource] : An Interactive Course in Combinatorial Optimization |
Author(s): |
Winfried Hochstttler Alexander Schliep |
Date: | 2010 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Graph algorithms are easy to visualize and indeed there already exists a variety of packages and programs to animate the dynamics when solving problems from graph theory. Still, and somewhat surprisingly, it can be difficult tounderstand the ideas be hind the algorithm from the dynamic display alone. CATBox consists of a software system for animating graph algorithms and a course book which we developed simultaneously. The software system presents both thealgorithm and the graph and puts the user alwa ys in control of the actual code that is executed. He or she can set breakpoints, proceed in single steps and trace into subroutines. The graph, and additional auxiliary graphs likeresidual networks, are displayed and provide visual feedback. The course b ook, intended for readers at advanced undergraduate or graduate level, introduces the ideas and discusses the mathematical background necessary for understandingand verifying the correctness of the algorithms and their complexity. Computer exercises and e xamples replace the usual static pictures of algorithm dynamics. For this volume we have chosen solely algorithms for classical problems fromcombinatorial optimization, such as minimum spanning trees, shortest paths, maximum flows, minimum cost flows as w ell as weighted and unweighted matchings both for bipartite and non-bipartite graphs. We consider non-bipartite weightedmatching, in particular in the geometrical case, a highlight of combinatorial optimization. In order to enable the reader to fully enjo y the beauty of the primal-dual solution algorithm for weighted matching, we present allmathematical material not only from the point of view of graph theory, but also with an emphasis on linear programming and its duality. This yields insightful and aest hetically pleasing pictures for matchings, but also for minimumspanning trees. You can find more information at http://schliep.org/CATBox/ |
Note: | Springer eBooks |
Contents: | Discrete Problems from Applications |
Basics, Notation and Data Structures | |
Minimum Spanning Trees | |
Linear Programming Duality | |
Shortest Paths | |
Maximal Flows | |
Minimum | |
Cost Flows | |
Matching | |
Weighted Matching | |
ISBN: | 9783642038228 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computational complexity , Combinatorics , Mathematical optimization , Operations research , Economics, Mathematical |
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Call number: | SPRINGER-2010-9781849962513:ONLINE Show nearby items on shelf |
Title: | Mathematica: A Problem-Centered Approach [electronic resource] |
Author(s): |
Roozbeh Hazrat |
Date: | 2010 |
Publisher: | London : Springer London |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Mathematica: A Problem-Centered Approach introduces the vast array of features and powerful mathematical functions of Mathematica using a multitude of clearly presented examples and worked- out problems. Each section startswith a description of a new topic and some basic examples. The author then demonstrates the use of new commands through three categories of problems - the first category highlights those essential parts of the text that demonstrate theuse of new commands in Mathematica whilst solvi ng each problem presented - the second comprises problems that further demonstrate the use of commands previously introduced to tackle different situations and - the third presents morechallenging problems for further study. The intention is to enable the reader to learn from the codes, thus avoiding long and exhausting explanations. While based on a computer algebra course taught to undergraduate students ofmathematics, science, engineering and finance, the book also includes chapters on calculus and sol ving equations, and graphics, thus covering all the basic topics in Mathematica. With its strong focus upon programming and problemsolving, and an emphasis on using numerical problems that do not need any particular background in mathematics, this book is also ideal for self-study and as an introduction to researchers who wish to use Mathematica as a computationaltool. Mathematica: A Problem-Centered Approach comes with a free 30 day trial of the Wolfram Mathematica(R) software |
Note: | Springer eBooks |
Contents: | 1. Introduction |
1.1 Mathematica as a calculator | |
1.2 Numbers | |
1.3 Algebraic computations | |
1.4 Trigonometric computations | |
1.5 Variables | |
1.6 Equalities =, :=, == | |
1.7 Dynamic variables | |
2. Defining functions | |
2.1 Formulas as functions | |
2.2 Anonymous functions | |
3. Lists | |
3.1 Functions producing lists | |
3.2 Listable functions | |
3.3 Selecting from a list | |
4. Changing heads! | |
5. A bit of logic and set theory | |
5.1 Being logical | |
5.2 Handling sets | |
5.3 Decision making, If and Which | |
6. Sums and products | |
6.1 Sum | |
6.2 Product | |
7. Loops and repetitions | |
7 | |
ISBN: | 9781849962513 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Undergraduate Mathematics Series, 1615-2085 : v53 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computer science , Algebra Data processing , Computer software |
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Call number: | SPRINGER-2010-9780817680862:ONLINE Show nearby items on shelf |
Title: | Optimal Control [electronic resource] |
Author(s): |
Richard Vinter |
Date: | 2010 |
Publisher: | Boston : Birkhuser Boston |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Optimal Control brings together many of the important advances in 'nonsmooth' optimal control over the last several decades concerning necessary conditions, minimizer regularity, and global optimality conditions associated withthe HamiltonJacobi equa tion. The book is largely self-contained and incorporates numerous simplifications and unifying features for the subjects key concepts and foundations. Features and Topics: * a comprehensive overview isprovided for specialists and nonspecialists * authori tative, coherent, and accessible coverage of the role of nonsmooth analysis in investigating minimizing curves for optimal control * chapter coverage of dynamic programming and theregularity of minimizers * explains the necessary conditions for nonconvex problems This book is an excellent presentation of the foundations and applications of nonsmooth optimal control for postgraduates, researchers, andprofessionals in systems, control, optimization, and applied mathematics. ----- Each chapter contains a wel l-written introduction and notes. They include the author's deep insights on the subject matter and provide historical commentsand guidance to related literature. This book may well become an important milestone in the literature of optimal control.Mathem atical Reviews This remarkable book presents Optimal Control seen as a natural development of Calculusof Variations so as to deal with the control of engineering devices. ... Thanks to a great effort to be self-contained, it renders accessibly the subject to a wide audience. Therefore, it is recommended to all researchers andprofessionals interested in Optimal Control and its engineering and economic applications. It can serve as an excellent textbook for graduate courses in Optimal Control (with special emphasis on Nonsmooth Analysis). Automatica Thebook may be an essential resource for potential readers, experts in control and optimization, as well as postgraduates and applied mathematicia |
Note: | Springer eBooks |
Contents: | Overview |
Measurable Multifunctions and Differential Inclusions | |
Variational Principles | |
Nonsmooth Analysis | |
Subdifferential Calculus | |
The Maximum Principle | |
The Extended EulerLagrange and Hamilton Conditions | |
Necessary Conditions for Free End | |
Time Problems | |
The Maximum Principle for State Constrained Problems | |
Necessary Conditions for Differential Inclusion Problems with State Constraints | |
Regularity of Minimizers | |
Dynamic Programming | |
ISBN: | 9780817680862 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Modern Birkhuser Classics |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Systems theory |
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Call number: | SPRINGER-2009-9783642044540:ONLINE Show nearby items on shelf |
Title: | Modelling, Pricing, and Hedging Counterparty Credit Exposure [electronic resource] : A Technical Guide |
Author(s): |
Giovanni Cesari John Aquilina Niels Charpillon Zlatko Filipovic Gordon Lee Ion Manda |
Date: | 2009 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Building an accurate representation of firm-wide credit exposure, used for both trading and risk management, raises significant theoretical and technical challenges. This volume can be considered as a roadmap to finding practicalsolutions to the prob lem of modelling, pricing, and hedging counterparty credit exposure for large portfolios of both vanilla and exotic derivatives, usually traded by large Investment Banks. It is divided into four parts, (I)Methodology, (II) Architecture and Implementation, (III) Products, and (IV) Hedging and Managing Counterparty Risk. Starting from a generic modelling and valuation framework based on American Monte Carlo techniques, it presents asoftware architecture, which, with its modular design, allows the computatio n of credit exposure in a portfolio-aggregated and scenario-consistent way. An essential part of the design is the definition of a programming language, whichallows trade representation based on dynamic modelling features. Several chapters are then devote d to the analysis of credit exposure across all asset classes, namely foreign exchange, interest rate, credit derivatives and equity.Finally it considers how to mitigate and hedge counterparty exposure. The crucial question of dynamic hedging is addressed by constructing a hybrid product, the Contingent-Credit Default Swap. This volume addresses, from a quantitativeperspective, recent developments related to counterparty credit exposure computation. Its unique characteristic is the combination of a rigoro us but simple mathematical approach with a practical view of the financial problem at hand....a fantastic book that covers all aspects of credit exposure modelling. Nowhere else can the interested reader find such a comprehensive collection of insights ar ound this topic covering methodology, implementation, products andapplications. A must read for practitioners and quants working in this space. Jrg Behrens, Fintegral Consulting, CH In the afterm |
Note: | Springer eBooks |
Contents: | Preface |
Glossary | |
Abbreviations and Notation | |
Part I: Methodology | |
1.Introduction | |
2.Modelling Framework | |
3.Simulation Models | |
4.Valuation and Sensitivities | |
Part II: Architecture and Implementation | |
5.Computational Framework | |
6.Implementation | |
7.Architecture | |
Part III: Products | |
8.Interest Rate Products | |
9.Equity, Commodity, Inflation and FX Products | |
10.Credit Derivatives | |
11.Hybrids and Structures | |
Part IV: Hedging and Managing Counterparty Risk | |
12.Counterparty Risk Aggregation and Risk Mitigation | |
13.Combining Market and Credit Risk | |
14.Pricing Cou | |
ISBN: | 9783642044540 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Finance |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Finance , Numerical analysis , Distribution (Probability theory) , Economics Statistics |
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Call number: | SPRINGER-2009-9783540895008:ONLINE Show nearby items on shelf |
Title: | Continuous-time Stochastic Control and Optimization with Financial Applications [electronic resource] |
Author(s): |
Huyn Pham |
Date: | 2009 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Stochastic optimization problems arise in decision-making problems under uncertainty, and find various applications in economics and finance. On the other hand, problems in finance have recently led to new developments in thetheory of stochastic cont rol. This volume provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backwardstochastic differential equations, and martingale duali ty methods. The theory is discussed in the context of recent developments in this field, with complete and detailed proofs, and is illustrated by means of concrete examples fromthe world of finance: portfolio allocation, option hedging, real options, opti mal investment, etc. This book is directed towards graduate students and researchers in mathematical finance, and will also benefit applied mathematiciansinterested in financial applications and practitioners wishing to know more about the use of stochast ic optimization methods in finance |
Note: | Springer eBooks |
Contents: | Some elements of stochastic analysis |
Stochastic optimization problems. Examples in finance | |
The classical PDE approach to dynamic programming | |
The viscosity solutions approach to stochastic control problems | |
Optimal switching and free boundary problems | |
Backward stochastic differential equations and optimal control | |
Martingale and convex duality methods | |
ISBN: | 9783540895008 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Stochastic Modelling and Applied Probability, 0172-4568 : v61 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Finance , Systems theory , Mathematical optimization , Distribution (Probability theory) |
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Call number: | SPRINGER-2009-9780387958712:ONLINE Show nearby items on shelf |
Title: | Fluid Dynamics [electronic resource] : Theory, Computation, and Numerical Simulation |
Author(s): |
Constantine Pozrikidis |
Date: | 2009 |
Edition: | 2 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Fluid Dynamics: Theory, Computation, and Numerical Simulation is the only available book that extends the classical field of fluid dynamics into the realm of scientific computing in a way that is both comprehensive and accessibleto the beginner. The theory of fluid dynamics, and the implementation of solution procedures into numerical algorithms, are discussed hand-in-hand and with reference to computer programming. This book is an accessible introduction totheoretical and computational fluid dynamic s (CFD), written from a modern perspective that unifies theory and numerical practice. There are several additions and subject expansions in the Second Edition of Fluid Dynamics, including newMatlab and FORTRAN codes. Two distinguishing features of the di scourse are: solution procedures and algorithms are developed immediately after problem formulations are presented, and numerical methods are introduced on a need-to-knowbasis and in increasing order of difficulty. Matlab codes are presented and discussed for a broad range of topics from interfacial shapes in hydrostatics, to vortex dynamics, to Stokes flow, to turbulent flow. A supplement to thisbook is the FORTRAN software library FDLIB, freely available through the Internet, whose programs explicitly i llustrate how computational algorithms translate into computer code instructions. The codes of FDLIB range from introductoryto advanced, and the problems considered span a broad range of applications from laminar channel flows, to vortex flows, to flows i n aerodynamics. Selected computer problems at the end of each section ask the student to run theprograms for various flow conditions, and thereby study the effect of the various parameters determining each flow. This text is a must for practitioners and s tudents in all fields of engineering, computational physics, scientificcomputing, and applied mathematics. It can be used as a text in both undergraduate and graduate courses in fluid mechanics, aerod |
Note: | Springer eBooks |
ISBN: | 9780387958712 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Physics and Astronomy (Springer-11651) |
Keywords: | Mathematical physics , Thermodynamics , Mechanics , Mechanical engineering |
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Call number: | SPRINGER-2009-9780387766171:ONLINE Show nearby items on shelf |
Title: | Stochastic Control in Discrete and Continuous Time [electronic resource] |
Author(s): |
Atle Seierstad |
Date: | 2009 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book provides a comprehensive introduction to stochastic control problems in discrete and continuous time. The material is presented logically, beginning with the discrete-time case before proceeding to the stochasticcontinuous-time models. Cent ral themes are dynamic programming in discrete time and HJB-equations in continuous time. Topics covered include stochastic maximum principles for discrete time and continuous time, even for problems withterminal conditions. Numerous illustrative examples and exercises, with solutions at the end of the book, are included to enhance the understanding of the reader. By interlinking many fields in stochastic control, the material givesthe student the opportunity to see the connections between different field s and the underlying ideas that unify them. This text will benefit students in applied mathematics, economics, engineering, and related fields. Prerequisitesinclude a course in calculus and elementary probability theory. No knowledge of measure theory is assumed |
Note: | Springer eBooks |
Contents: | Preface |
Stochastic Control over Discrete Time | |
The HJB Equation for Deterministic Control | |
Piecewise Deterministic Optimal Control Problems | |
Control of Diffusions | |
Appendix: Probability, Concepts, and Results | |
Solutions | |
References | |
Index | |
ISBN: | 9780387766171 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Systems theory , Mathematical optimization , Distribution (Probability theory) , Economics, Mathematical |
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Call number: | SPRINGER-2008-9780817646066:ONLINE Show nearby items on shelf |
Title: | Set-Theoretic Methods in Control [electronic resource] |
Author(s): |
Franco Blanchini Stefano Miani |
Date: | 2008 |
Publisher: | Boston, MA : Birkhuser Boston |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This self-contained monograph describes basic set-theoretic methods for control and provides a discussion of their links to fundamental problems in Lyapunov stability analysis and stabilization, optimal control, control underconstraints, persistent d isturbance rejection, and uncertain systems analysis and synthesis. New computer technology has catalyzed a resurgence of research in this area, particularly in the development of set-theoretic techniques, manyof which are computationally demanding. The w ork presents several established and potentially new applications, along with numerical examples and case studies. A key theme of the presentation is the trade-off between exact (butcomputationally intensive) and approximate (but conservative) solutions t o problems. Mathematical language is kept to the minimum necessary for the adequate formulation and statement of main concepts. Numerical algorithms for thesolution of the proposed problems are described in detail. Set-Theoretic Methods in Control is acce ssible to readers familiar with the basics of systems and control theory prerequisites such as convexity theory are included. The textprovides a solid foundation of mathematical techniques and applications and also features avenues for further theoretical study. Aimed primarily at graduate students and researchers in applied mathematics and engineering, the book willalso appeal to practitioners since it contains extensive references to the literature and supplies many recipes for solving significant contr ol problems |
Note: | Springer eBooks |
Contents: | Preface |
Introduction | |
Lyapunov and Lyapunov | |
like Functions | |
Convex Sets and Their Representation | |
Invariant Sets | |
Dynamic Programming | |
Set | |
Theoretic Analysis of Dynamic Systems | |
Control of Parameter | |
Varying Systems | |
Control with Time | |
Domain Constraints | |
(Sub | |
)Optimal Control | |
Set | |
Theoretic Estimation | |
Related Topics | |
Appendix | |
References | |
Index | |
ISBN: | 9780817646066 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Systems & Control: Foundations & Applications |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Systems theory , Mathematical optimization , Engineering mathematics |
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Call number: | SPRINGER-2008-9780387768137:ONLINE Show nearby items on shelf |
Title: | Fuzzy Multi-Criteria Decision Making [electronic resource] : Theory and Applications with Recent Developments |
Author(s): |
Cengiz Kahraman |
Date: | 2008 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | In trying to make a satisfactory decision when imprecise and multicriteria situations are involved, a decision maker has to use a fuzzy multicriteria decision making method. Fuzzy Multi-Criteria Decision Making (MCDM) presentsfuzzy multiattribute and multiobjective decision-making methodologies by distinguished MCDM researchers. In summarizing the concepts and results of the most popular fuzzy multicriteria methods, using numerical examples, this workexamines all the fuzzy multicriteria methods recen tly developed, such as fuzzy AHP, fuzzy TOPSIS, interactive fuzzy multiobjective stochastic linear programming, fuzzy multiobjective dynamic programming, grey fuzzy multiobjectiveoptimization, fuzzy multiobjective geometric programming, and more. Each of the 22 chapters includes practical applications along with new developments/results. This book may be used as a textbook in graduate operations research,industrial engineering, and economics courses. It will also be an excellent resource, providing new su ggestions and directions for further research, for computer programmers, mathematicians, and scientists in a variety of disciplineswhere multicriteria decision making is needed |
Note: | Springer eBooks |
Contents: | Fuzzy MADM Methods and Applications |
Multi | |
Criteria Decision Making Methods and Fuzzy Sets | |
Intelligent Fuzzy Multi | |
Criteria Decision Making: Review and Analysis | |
Fuzzy Analytic Hierarchy Process and its Application | |
A SWOT | |
AHP Application Using Fuzzy Concept: E | |
Government in Turkey | |
Fuzzy Outranking Methods: Recent Developments | |
Fuzzy Multi | |
Criteria Evaluation of Industrial Robotic Systems Using Topsis | |
Fuzzy Multi | |
Attribute Scoring Methods with Applications | |
Fuzzy Multi | |
Attribute Decision Making Using an Information Axiom | |
Based Approach | |
Measurement of Level | |
of | |
Satisfaction | |
ISBN: | 9780387768137 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Optimization and Its Applications, 1931-6828 : v16 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Mathematical optimization , Operations research , Engineering mathematics |
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Call number: | SPRINGER-2008-9780387766355:ONLINE Show nearby items on shelf |
Title: | Introduction to Applied Optimization [electronic resource] |
Author(s): |
Urmila Diwekar |
Date: | 2008 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This text presents a multi-disciplined view of optimization, providing students and researchers with a thorough examination of algorithms, methods, and tools from diverse areas of optimization without introducing excessivetheoretical detail. This sec ond edition includes additional topics, including global optimization and a real-world case study using important concepts from each chapter. Key Features: Provides well-written self-contained chapters,including problem sets and exercises, making it ideal for the classroom setting Introduces applied optimization to the hazardous waste blending problem Explores linear programming, nonlinear programming, discrete optimization, globaloptimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control Includes an extensive bibliography at the end of each chapter and an index GAMS files of case studies forChapters 2, 3, 4, 5, and 7 are linked to http://www.springer.com/math/book/978-0-387-76634-8 Solutions manual available upon adoptions. Introduction to Applied Optimization is intended for advanced undergraduate and graduate studentsand will benefit scientists from diverse areas, including engineers |
Note: | Springer eBooks |
Contents: | Linear Programming |
Nonlinear Programming | |
Discrete Optimization | |
Optimization Under Uncertainty | |
Multiobjective Optimization | |
Optimal Control and Dynamic Optimization | |
ISBN: | 9780387766355 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Optimization and Its Applications, 1931-6828 : v22 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Chemical engineering , Systems theory , Mathematical optimization , Engineering mathematics , Economics |
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Call number: | SPRINGER-2008-9780387758169:ONLINE Show nearby items on shelf |
Title: | Stochastic Control of Hereditary Systems and Applications [electronic resource] |
Author(s): |
Mou-Hsiung Chang |
Date: | 2008 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This research monograph develops the Hamilton-Jacobi-Bellman (HJB) theory through dynamic programming principle for a class of optimal control problems for stochastic hereditary differential systems. It is driven by a standardBrownian motion and with a bounded memory or an infinite but fading memory. The optimal control problems treated in this book include optimal classical control and optimal stopping with a bounded memory and over finite time horizon.This book can be used as an introduction for re searchers and graduate students who have a special interest in learning and entering the research areas in stochastic control theory with memories. Each chapter contains a summary.Mou-Hsiung Chang is a program manager at the Division of Mathematical Scien ces for the U.S. Army Research Office |
Note: | Springer eBooks |
Contents: | Preface |
Introduction and Summary | |
Chapter 1. Stochastic Hereditary Differential Equations | |
Chapter 2. Stochastic Calculus | |
Chapter 3. Optimal Classical Control | |
Chapter 4. Optimal Stopping | |
Chapter 5. Discrete Approximations | |
Chapter 6. Option Pricing | |
Chapter 7. Hereditary Portfolio Optimization | |
References | |
List of Symbols | |
Index | |
ISBN: | 9780387758169 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Stochastic Modelling and Applied Probability, 0172-4568 : v59 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Differential equations, partial , Distribution (Probability theory) , Mathematical statistics |
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Call number: | SPRINGER-2008-9780387732992:ONLINE Show nearby items on shelf |
Title: | Optimization in Medicine [electronic resource] |
Author(s): |
Carlos J. S Alves Panos M Pardalos Luis Nunes Vicente |
Date: | 2008 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Optimization has become an essential tool in addressing the limitation of resources and need for better decision-making in the medical field. Both continuous and discrete mathematical techniques are playing an increasinglyimportant role in understand ing several fundamental problems in medicine. This volume presents a wide range of medical applications that can utilize mathematical computing. Examples include using an algorithm for considering the seedreconstruction problem in brachytherapy and using optimization-classification models to assist in the early prediction, diagnosis and detection of diseases. Discrete optimization techniques and measures derived from the theory ofnonlinear dynamics, with analysis of multi-electrode electroencephalographic (EEG) data, assist in predicting impending epileptic seizures. Mathematics in medicine can also be found in recent cancer research. Sophisticated mathematicalmodels and optimization algorithms have been used to generate treatment plans for radionuclide i mplant and external beam radiation therapy. Optimization techniques have also been used to automate the planning process in Gamma Knifetreatment, as well as to address a variety of medical image registration problems. This work grew out of a workshop on o ptimization which was held during the 2005 CIM Thematic Term on Optimization in Coimbra, Portugal. It provides anoverview of the state-of-the-art in optimization in medicine and will serve as an excellent reference for researchers in the medical computing community and for those working in applied mathematics and optimization |
Note: | Springer eBooks |
Contents: | The influence of dose grid resolution on beam selection strategies in radiotherapy treatment design |
Decomposition of matrices and static multileaf collimators: a survey | |
Neuro | |
dynamic programming for fractionated radiotherapy planning | |
Randomized algorithms for mixed matching and covering in hypergraphs in 3D seed reconstruction in brachytherapy | |
Global optimization and spatial synchronization changes prior to epileptic seizures | |
Optimization | |
based predictive models in medicine and biology | |
Optimal reconstruction kernels in medical imaging | |
Optimal control in high intensity focu | |
ISBN: | 9780387732992 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Optimization and Its Applications, 1931-6828 : v12 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Radiology, Medical , Mathematical optimization |
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Call number: | SPRINGER-2008-9780387727431:ONLINE Show nearby items on shelf |
Title: | Convergence and Applications of Newton-type Iterations [electronic resource] |
Author(s): |
Ioannis K Argyros |
Date: | 2008 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Recent results in local convergence and semi-local convergence analysis constitute a natural framework for the theoretical study of iterative methods. This monograph provides a comprehensive study of both basic theory and newresults in the area. Each chapter contains new theoretical results and important applications in engineering, modeling dynamic economic systems, input-output systems, optimization problems, and nonlinear and linear differentialequations. Several classes of operators are considere d, including operators without Lipschitz continuous derivatives, operators with high order derivatives, and analytic operators. Each section is self-contained. Examples are used toillustrate the theory and exercises are included at the end of each chapter . The book assumes a basic background in linear algebra and numerical functional analysis. Graduate students and researchers will find this book useful. It maybe used as a self-study reference or as a supplementary text for an advanced course in numerical functional analysis |
Note: | Springer eBooks |
Contents: | Preface |
Table of Contents | |
Operators and Equations | |
The Newton | |
Kantorovich (NK) Method | |
Applications of the Weaker Version of the (NK) Theorem | |
Special Methods | |
Newton | |
Like Methods | |
Analytic Computational Complexity: We are Concerned with the Choice of Initial Approximations | |
Variational Inequalities | |
Convergence Involving Operators with Outer or Generalized Inverses | |
Convergence on Generalized Banach Spaces | |
Point | |
to | |
Set Mappings | |
The Newton | |
Kantorovich Theorem and Mathematical Programming | |
Glossary of Symbols | |
Bibliography | |
ISBN: | 9780387727431 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Functional analysis , Computer science Mathematics , Numerical analysis |
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Call number: | SPRINGER-2008-9780387717623:ONLINE Show nearby items on shelf |
Title: | Interactive and Dynamic Graphics for Data Analysis [electronic resource] : With R and Ggobi |
Author(s): |
Dianne Cook Deborah F Swayne |
Date: | 2008 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with missing values. A variety ofplots and interaction m ethods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each stepof the analysis, not only in the early expl oratory phase, but in the later stages, too, when comparing and evaluating models. All examples are based on freely available software: GGobi for interactive graphics and R for staticgraphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses inthe book, along with movies demonstrating the examples. The book may be used as a text in a cla ss on statistical graphics or exploratory data analysis, for example, or as a guide for the independent learner. Each chapter ends with aset of exercises. The authors are both Fellows of the American Statistical Association, past chairs of the Section on Statistical Graphics, and co-authors of the GGobi software. Dianne Cook is Professor of Statistics at Iowa StateUniversity. Deborah Swayne is a member of the Statistics Research Department at AT&T Labs |
Note: | Springer eBooks |
Contents: | Introduction |
The toolbox | |
Missing values | |
Supervised classification | |
Cluster analysis | |
Miscellaneous topics | |
Data sets | |
ISBN: | 9780387717623 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Use R! |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Data mining , Bioinformatics , Visualization , Mathematical statistics |
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Call number: | SPRINGER-2008-9780387369518:ONLINE Show nearby items on shelf |
Title: | Markov Decision Processes With Their Applications [electronic resource] |
Author(s): |
Qiying Hu Wuyi Yue |
Date: | 2008 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochasticcircumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practicalproblems. These models include partially obse rvable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters. Markov Decision Processes With Their Applicationsexamines MDPs and their applications in the optimal control of discrete e vent systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents four main topics that are used to study optimalcontrol problems: *a new methodology for MDPs with discounted total reward criterion *transfo rmation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application ofMDPs *MDPs in stochastic environments, which greatly extends the area where MDPs can be applied *applications of MDPs in optimal control of discrete event systems, optimal replacement, and optimal allocation in sequential onlineauctions. This book is intended for researchers, mathematicians, advanced graduate students, and engineers who are interested in optimal control , operation research, communications, manufacturing, economics, and electronic commerce |
Note: | Springer eBooks |
Contents: | Discretetimemarkovdecisionprocesses: Total Reward |
Discretetimemarkovdecisionprocesses: Average Criterion | |
Continuous Time Markov Decision Processes | |
Semi | |
Markov Decision Processes | |
Markovdecisionprocessesinsemi | |
Markov Environments | |
Optimal control of discrete event systems: I | |
Optimal control of discrete event systems: II | |
Optimal replacement under stochastic Environments | |
Optimalal location in sequential online Auctions | |
ISBN: | 9780387369518 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Advances in Mechanics and Mathematics : v14 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Mathematical optimization , Operations research , Distribution (Probability theory) , Industrial engineering |
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Call number: | SPRINGER-2007-9784431727613:ONLINE Show nearby items on shelf |
Title: | Advances in Mathematical Economics [electronic resource] |
Author(s): |
Shigeo Kusuoka Akira Yamazaki |
Date: | 2007 |
Publisher: | Tokyo : Springer Japan |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | A lot of economic problems can formulated as constrained optimizations and equilibration of their solutions. Various mathematical theories have been supplying economists with indispensable machineries for these problems arising ineconomic theory. Con versely, mathematicians have been stimulated by various mathematical difficulties raised by economic theories. The series is designed to bring together those mathematicians who were seriously interested in gettingnew challenging stimuli from economic theo ries with those economists who are seeking for effective mathematical tools for their researchers. Members of the editorial board of this series consists of following prominent economists andmathematicians: Managing Editors: S. Kusuoka (Univ. Tokyo), A. Y amazaki (Hitotsubashi Univ.) - Editors: R. Anderson (U.C.Berkeley), C. Castaing (Univ. Montpellier II), F. H. Clarke (Univ. Lyon I), E. Dierker (Univ. Vienna), D. Duffie(Stanford Univ.), L.C. Evans (U.C. Berkeley), T. Fujimoto (Fukuoka Univ.), J. -M. Gran dmont (CREST-CNRS), N. Hirano (Yokohama National Univ.), L. Hurwicz (Univ. of Minnesota), T. Ichiishi (Hitotsubashi Univ.), A. Ioffe (IsraelInstitute of Technology), S. Iwamoto (Kyushu Univ.), K. Kamiya (Univ. Tokyo), K. Kawamata (Keio Univ.), N. Kikuchi (Keio Univ.), T. Maruyama (Keio Univ.), H. Matano (Univ. Tokyo), K. Nishimura (Kyoto Univ.), M. K. Richter (Univ.Minnesota), Y. Takahashi (Kyoto Univ.), M. Valadier (Univ. Montpellier II), M. Yano (Keio Univ) |
Note: | Springer eBooks |
Contents: | Komls type convergence for random variables and random sets with applications to minimization problems |
Capital | |
labor substitution and indeterminacy in continuous | |
time two | |
sector models | |
Weak and strong convergence theorems for new resolvents of maximal monotone operators in Banach spaces | |
Golden optimal policy in calculus of variation and dynamic programming | |
A remark on law invariant convex risk measures | |
Existence and uniqueness of an equilibrium in a model of spatial electoral competition with entry | |
Publishers Errata Solving long term optimal investment problems with Cox | |
ISBN: | 9784431727613 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Advances in Mathematical Economics : v10 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Economics |
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Call number: | SPRINGER-2007-9783540698265:ONLINE Show nearby items on shelf |
Title: | Applied Stochastic Control of Jump Diffusions [electronic resource] |
Author(s): |
Bernt ksendal Agns Sulem |
Date: | 2007 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The main purpose of the book is to give a rigorous, yet mostly nontechnical, introduction to the most important and useful solution methods of various types of stochastic control problems for jump diffusions and its applications.The types of control problems covered include classical stochastic control, optimal stopping, impulse control and singular control. Both the dynamic programming method and the maximum principle method are discussed, as well as therelation between them. Corresponding verificat ion theorems involving the Hamilton-Jacobi Bellman equation and/or (quasi-)variational inequalities are formulated. There are also chapters on the viscosity solution formulation andnumerical methods. The text emphasises applications, mostly to finance. Al l the main results are illustrated by examples and exercises appear at the end of each chapter with complete solutions. This will help the reader understand thetheory and see how to apply it. The book assumes some basic knowledge of stochastic analysis, m easure theory and partial differential equations. In the 2nd edition there is a new chapter on optimal control of stochastic partialdifferential equations driven by Lvy processes. There is also a new section on optimal stopping with delayed information. M oreover, corrections and other improvements have been made |
Note: | Springer eBooks |
Contents: | Stochastic Calculus with Jump Diffusions |
Optimal Stopping of Jump Diffusions | |
Stochastic Control of Jump Diffusions | |
Combined Optimal Stopping and Stochastic Control of Jump Diffusions | |
Singular Control for Jump Diffusions | |
Impulse Control of Jump Diffusions | |
Approximating Impulse Control by Iterated Optimal Stopping | |
Combined Stochastic Control and Impulse Control of Jump Diffusions | |
Viscosity Solutions | |
Optimal Control of Random Jump Fields and Partial Information Control | |
Solutions of Selected Exercises | |
ISBN: | 9783540698265 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Universitext |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Operator theory , Finance , Operations research , Distribution (Probability theory) |
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Call number: | SPRINGER-2007-9780387389837:ONLINE Show nearby items on shelf |
Title: | Bayesian Core: A Practical Approach to Computational Bayesian Statistics [electronic resource] |
Author(s): |
Jean-Michel Marin Christian P Robert |
Date: | 2007 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed realdatasets available fro m the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of priordistributions in each case and specific reference sol utions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. While Rprograms are provided on the book website and R hints are given in the co mputational sections of the book, The Bayesian Core requires no knowledge of the R language and it can be read and used with any other programming language. TheBayesian Core can be used as a textbook at both undergraduate and graduate levels, as exemplifi ed by courses given at Universit Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia(Canada). It serves as a unique textbook for a service course for scientists aiming at analyzing data the Bayesian way as w ell as an introductory course on Bayesian statistics. The prerequisites for the book are a basic knowledge ofprobability theory and of statistics. Methodological and data-based exercises are included within the main text and students are expected to solve them as they read the book. Those exercises can obviously serve as assignments, as wasdone in the above courses. Datasets, R codes and course slides all are available on the book website. Jean-Michel Marin is currently senior researcher at INRIA, the Fre nch Computer Science research institute, and located at UniversitParis-Sud, Orsay. He has previously been Assistant Professor at Universit Paris Dauphine for four years. He has written numerous pa |
Note: | Springer eBooks |
Contents: | User's manual |
Normal models | |
Regression and variable selection | |
Generalised linear models | |
Capture | |
recapture experiments | |
Mixture models | |
Dynamic models | |
Image analysis | |
ISBN: | 9780387389837 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Texts in Statistics, 1431-875X |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer science , Computer simulation , Mathematical statistics |
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Call number: | SPRINGER-2006-9780817644536:ONLINE Show nearby items on shelf |
Title: | Max-Plus Methods for Nonlinear Control and Estimation [electronic resource] |
Author(s): |
William M McEneaney |
Date: | 2006 |
Publisher: | Boston, MA : Birkhuser Boston |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The central focus of this book is the control of continuous-time/continuous-space nonlinear systems. Using new techniques that employ the max-plus algebra, the author addresses several classes of nonlinear control problems,including nonlinear optimal control problems and nonlinear robust/H-infinity control and estimation problems. Several numerical techniques are employed, including a max-plus eigenvector approach and an approach that avoids thecurse-of-dimensionality. Well-known dynamic programming arguments show there is a direct relationship between the solution of a control problem and the solution of a corresponding HamiltonJacobiBellman (HJB) partial differentialequation (PDE). The max-plus-based methods examined in this monograph belong to an entirely new class of numerical methods for the solution of nonlinear control problems and their associated HJB PDEs they are not equivalent to eitherof the more commonly used finite element or characteristic approaches. The potential advantages of the ma x-plus-based approaches lie in the fact that solution operators for nonlinear HJB problems are linear over the max-plus algebra,and this linearity is exploited in the construction of algorithms. The book will be of interest to applied mathematicians, engi neers, and graduate students interested in the control of nonlinear systems through the implementation ofrecently developed numerical methods. Researchers and practitioners tangentially interested in this area will also find a readable, concise discussion of the subject through a careful selection of specific chapters and sections. Basicknowledge of control theory for systems with dynamics governed by differential equations is required |
Note: | Springer eBooks |
Contents: | Preface |
Introduction | |
Max | |
Plus Analysis | |
Dynamic Programming and Viscosity Solutions | |
Max | |
Plus Eigenvector Method for the Infinite Horizon Problem | |
Max | |
Plus Eigenvector Method Error Analysis | |
A Semigroup Construction Method | |
Curse | |
of | |
Dimensionality | |
Free Method | |
Finite Time | |
Horizon Application: Nonlinear Filtering | |
Mixed L | |
infinity/L2 Criteria | |
Miscellaneous Proofs | |
References | |
Index | |
ISBN: | 9780817644536 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Systems & Control: Foundations & Applications |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Differential equations, partial , Systems theory , Computer science Mathematics , Engineering mathematics |
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Call number: | SPRINGER-2006-9780387310718:ONLINE Show nearby items on shelf |
Title: | Controlled Markov Processes and Viscosity Solutions [electronic resource] |
Author(s): |
Wendell H Fleming H.M Soner |
Date: | 2006 |
Edition: | Second Edition |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book is intended as an introduction to optimal stochastic control for continuous time Markov processes and to the theory of viscosity solutions. Stochastic control problems are treated using the dynamic programming approach.The authors approach stochastic control problems by the method of dynamic programming. The fundamental equation of dynamic programming is a nonlinear evolution equation for the value function. For controlled Markov diffusion processes,this becomes a nonlinear partial differen tial equation of second order, called a Hamilton-Jacobi-Bellman (HJB) equation. Typically, the value function is not smooth enough to satisfy the HJB equation in a classical sense. Viscositysolutions provide framework in which to study HJB equations, and to prove continuous dependence of solutions on problem data. The theory is illustrated by applications from engineering, management science, and financial economics. Inthis second edition, new material on applications to mathematical finance has been adde d. Concise introductions to risk-sensitive control theory, nonlinear H-infinity control and differential games are also included. Review of theearlier edition: This book is highly recommended to anyone who wishes to learn the dinamic principle applied to optimal stochastic control for diffusion processes. Without any doubt, this is a fine book and most likely it is going tobecome a classic on the area... . SIAM Review, 1994 |
Note: | Springer eBooks |
Contents: | Preface |
Preface to Second Edition | |
Notation | |
Deterministic Optimal Control | |
Viscosity Solutions | |
Optimal Control of Markov Processes:Classical Solutions | |
Controlled Markov Diffusions in IRn | |
Viscosity Solutions: Scond | |
Order Case | |
Logarithmic Transformations and Risk Sensitivity | |
Singular Perturbations | |
Singular Stochastic Control | |
Finite Difference Numerical Approximations | |
Applications to Finance | |
Differential Games | |
Duality Relationships | |
Dynkin s Formula for Random Evolutions with Markov Chain Parameters | |
Extension of Lipschitz Continuous Functions Smoot | |
ISBN: | 9780387310718 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Stochastic Modelling and Applied Probability, 0172-4568 : v25 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Finance , Systems theory , Distribution (Probability theory) |
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Call number: | SPRINGER-2006-9780387301655:ONLINE Show nearby items on shelf |
Title: | Handbook of Optimization in Telecommunications [electronic resource] |
Author(s): |
Mauricio G. C Resende Panos M Pardalos |
Date: | 2006 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | I highly recommend The Handbook of Optimization in Telecommunications as an invaluable resource for anyone interested in understanding the impact of optimization on the most import problems facing the telecommunicationsindustry today. The handbook is unprecedented in the breadth and depth of its coverage, illustrating that telecommunications offers a vast array of interesting and important optimization problems probably exceeding the traditionalareas of transportation networks, engineering, economics and military operations. Clyde Monma, Retired Chief Scientist, Applied Research Area, Telcordia Technologies Telecommunications has had a major impact in all aspects oflife in the last century. There is little doubt that the transformation from the indus trial age to the information age has been fundamentally influenced by advances in telecommunications. Optimization problems are abundant in thetelecommunications industry. The successful solution of these problems has played an important role in the devel opment of telecommunications and its widespread use. Optimization problems arise in the design of telecommunication systemsand in their operation. The Handbook of Optimization in Telecommunications brings together experts from around the world who use opt imization to solve problems that arise in telecommunications. The editors made an effort to coverrecent optimization developments that are frequently applied to telecommunications. The spectrum of topics covered includes planning and design of telecommuni cation networks, routing, network protection, grooming, restoration, wirelesscommunications, network location and assignment problems, Internet protocol, World Wide Web, and stochastic issues in telecommunications. The editors objective is to provide a re ference tool for the increasing number of scientistsand engineers in telecommunications who depend upon optimization in some way. Each chapter in the handbook is of an expository nature, but o |
Note: | Springer eBooks |
Contents: | From the contents: Preface |
Part I. Optimization Algorithms | |
Interior Point Methods for Large | |
scale Linear Programming | |
Nonlinear Programming in Telecommunications | |
Integer Programming for Telecommunications | |
Metaheuristics and Applications to Optimization Problems in Telecommunications | |
Lagrangian relax | |
and | |
cut Algorithms | |
Minimum Cost Network OW Algorithms | |
Multicommodity Network OW Models and Algorithms in Telecommunications | |
Shortest Path Algorithms | |
Part II. Planning and Dedign | |
Network planning | |
Part III. Routing | |
Optimization of Dynamic Routing Networks | |
Part | |
ISBN: | 9780387301655 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Mathematical optimization , Telecommunication |
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Call number: | SPRINGER-2004-9783662064047:ONLINE Show nearby items on shelf |
Title: | Control Theory from the Geometric Viewpoint |
Author(s): |
Andrei A Agrachev |
Date: | 2004 |
Size: | 1 online resource (412 p.) |
Note: | 10.1007/978-3-662-06404-7 |
Contents: | 1 Vector Fields and Control Systems on Smooth Manifolds -- 2 Elements of Chronological Calculus -- 3 Linear Systems -- 4 State Linearizability of Nonlinear Systems -- 5 The Orbit Theorem and its Applications -- 6 Rotations of the |
Rigid Body -- 7 Control of Configurations -- 8 Attainable Sets -- 9 Feedback and State Equivalence of Control Systems -- 10 Optimal Control Problem -- 11 Elements of Exterior Calculus and Symplectic Geometry -- 12 Pontryagin Maximum | |
Principle -- 13 Examples of Optimal Control Problems -- 14 Hamiltonian Systems with Convex Hamiltonians -- 15 Linear Time-Optimal Problem -- 16 Linear-Quadratic Problem -- 17 Sufficient Optimality Conditions, Hamilton-Jacobi Equation, | |
and Dynamic Programming -- 18 Hamiltonian Systems for Geometric Optimal Control Problems -- 19 Examples of Optimal Control Problems on Compact Lie Groups -- 20 Second Order Optimality Conditions -- 21 Jacobi Equation -- 22 Reduction -- | |
23 Curvature -- 24 Rolling Bodies -- A Appendix -- A.2 Remainder Term of the Chronological Exponential -- References -- List of Figures | |
ISBN: | 9783662064047 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Series: | Encyclopaedia of Mathematical Sciences, Control Theory and Optimization II: 87 |
Keywords: | Mathematics , System theory , Mathematics , Systems Theory, Control |
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Call number: | SPRINGER-2004-9783642558849:ONLINE Show nearby items on shelf |
Title: | Dynamic Stochastic Optimization |
Author(s): | |
Date: | 2004 |
Size: | 1 online resource (336 p.) |
Note: | 10.1007/978-3-642-55884-9 |
Contents: | I. Dynamic Decision Problems under Uncertainty: Modeling Aspects -- Reflections on Output Analysis for Multistage Stochastic Linear Programs -- Modeling Support for Multistage Recourse Problems -- Optimal Solutions for Undiscounted |
Variance Penalized Markov Decision Chains -- Approximation and Optimization for Stochastic Networks -- II. Dynamic Stochastic Optimization in Finance -- Optimal Stopping Problem and Investment Models -- Estimating LIBOR/Swaps | |
Spot-Volatilities: the EpiVolatility Model -- Structured Products for Pension Funds -- III. Optimal Control Under Stochastic Uncertainty -- Real-time Robust Optimal Trajectory Planning of Industrial Robots -- Adaptive Optimal | |
Stochastic Trajectory Planning and Control (AOSTPC) for Robots -- IV. Tools for Dynamic Stochastic Optimization -- Solving Stochastic Programming Problems by Successive Regression Approximations — Numerical Results -- Stochastic | |
Optimization of Risk Functions via Parametric Smoothing -- Optimization under Uncertainty using Momentum -- Perturbation Analysis of Chance-constrained Programs under Variation of all Constraint Data -- The Value of Perfect Information | |
as a Risk Measure -- New Bounds and Approximations for the Probability Distribution of the Length of the Critical Path -- Simplification of Recourse Models by Modification of Recourse Data | |
ISBN: | 9783642558849 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Series: | Lecture Notes in Economics and Mathematical Systems: 532 |
Keywords: | Mathematics , Operations research , Decision making , System theory , Calculus of variations , Mathematics , Systems Theory, Control , Operation Research/Decision Theory , Calculus of Variations and Optimal Control Optimization |
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Call number: | SPRINGER-2004-9783540247777:ONLINE Show nearby items on shelf |
Title: | Knapsack Problems |
Author(s): |
Hans Kellerer |
Date: | 2004 |
Size: | 1 online resource (548 p.) |
Note: | 10.1007/978-3-540-24777-7 |
Contents: | 1 Introduction -- 1.1 Introducing the Knapsack Problem -- 1.2 Variants and Extensions of the Knapsack Pr©blem -- 1.3 Single-Capacity Versus All-Capacities Problem -- 1.4 Assumptions on the Input Data -- 1.5 Performance of Algorithms |
-- 2. Basic Algorithmic Concepts -- 2.1 The Greedy Algorithm -- 2.2 Linear Programming Relaxation -- 2.3 Dynamic Programming -- 2.4 Branch-and-Bound -- 2.5 Approximation Algorithms -- 2.6 Approximation Schemes -- 3. Advanced | |
Algorithmic Concepts -- 3.1 Finding the Split Item in Linear Time -- 3.2 Variable Reduction -- 3.3 Storage Reduction in Dynamic Programming -- 3.4 Dynamic Programming with Lists -- 3.5 Combining Dynamic Programming and Upper Bounds -- | |
3.6 Balancing -- 3.7 Word RAM Algorithms -- 3.8 Relaxations -- 3.9 Lagrangian Decomposition -- 3.10 The Knapsack Polytope -- 4. The Subset Sum Problem -- 4.1 Dynamic Programming -- 4.2 Branch-and-Bound -- 4.3 Core Algorithms -- 4.4 | |
Computational Results: Exact Algorithms -- 4.5 Polynomial Time Approximation Schemes for Subset Sum -- 4.6 A Fully Polynomial Time Approximation Scheme for Subset Sum -- 4.7 Computational Results: FPTAS -- 5. Exact Solution of the | |
Knapsack Problem -- 5.1 Branch-and-Bound -- 5.2 Primal Dynamic Programming Algorithms -- 5.3 Primal-Dual Dynamic Programming Algorithms -- 5.4 The Core Concept -- 5.5 Computational Experiments -- 6. Approximation Algorithms for the | |
Knapsack Problem -- 6.1 Polynomial Time Approximation Schemes -- 6.2 Fully Polynomial Time Approximation Schemes -- 7. The Bounded Knapsack Problem -- 7.1 Introduction -- 7.2 Dynamic Programming -- 7.3 Branch-and-Bound -- 7.4 | |
Approximation Algorithms -- 8. The Unbounded Knapsack Problem -- 8.1 Introduction -- 8.2 Periodicity and Dominance -- 8.3 Dynamic Programming -- 8.4 Branch-and-Bound -- 8.5 Approximation Algorithms -- 9 Multidimensional Knapsack | |
Problems -- 9.1 Introduction -- 9.2 Relaxations and Reductions -- 9.3 Exact Algorithms -- 9.4 Approximation -- 9.5 Heuristic Algorithms -- 9.6 The Two-Dimensional Knapsack Problem -- 9.7 The Cardinality Constrained Knapsack Problem -- | |
9.8 The Multidimensional Multiple-Choice Knapsack Problem -- 10. Multiple Knapsack Problems -- 10.1 Introduction -- 10.2 Upper Bounds -- 10.3 Branch-and-Bound -- 10.4 Approximation Algorithms -- 10.5 Polynomial Time Approximation | |
Schemes -- 10.6 Variants of the Multiple Knapsack Problem -- 11. The Multiple-Choice Knapsack Problem -- 11.1 Introduction -- 11.2 Dominance and Upper Bounds -- 11.3 Class Reduction -- 11.4 Branch-and-Bound -- 11.5 Dynamic Programming | |
-- 11.6 Reduction of States -- 11.7 Hybrid Algorithms and Expanding Core Algorithms -- 11.8 Computational Experiments -- 11.9 Heuristics and Approximation Algorithms -- 11.10 Variants of the Multiple-Choice Knapsack Problem -- 12. The | |
Quadratic Knapsack Problem -- 12.1 Introduction -- 12.2 Upper Bounds -- 12.3 Variable Reduction -- 12.4 Branch-and-Bound -- 12.5 The Algorithm by Caprara, Pisinger and Toth -- 12.6 Heuristics -- 12.7 Approximation Algorithms -- 12.8 | |
Computational Experiments Exact Algorithms -- 12.9 Computational Experiments Upper Bounds -- 13. Other Knapsack Problems -- 13.1 Multiobjective Knapsack Problems -- 13.2 The Precedence Constraint Knapsack Problem (PCKP) -- 13.3 Further | |
Variants -- 14. Stochastic Aspects of Knapsack Problems -- 14.1 The Probabilistic Model -- 14.2 Structural Results -- 14.3 Algorithms with Expected Performance Guarantee -- 14.4 Expected Performance of Greedy-Type Algorithms -- 14.5 | |
Algorithms with Expected Running Time -- 14.6 Results for the Subset Sum Problem -- 14.7 Results for the Multidimensional Knapsack Problem -- 14.8 The On-Line Knapsack Problem -- 15. Some Selected Applications -- 15.1 Two-Dimensional | |
Two-Stage Cutting Problems -- 15.2 Column Generation in Cutting Stock Problems -- 15.3 Separation of Cover Inequalities -- 15.4 Financial Decision Problems -- 15.5 Asset-Backed Securitization -- 15.6 Knapsack Cryptosystems -- 15.7 | |
Combinatorial Auctions -- A. Introduction to NP-Completeness of Knapsack Problems -- A.1 Definitions -- A.2 NP-Completeness of the Subset Sum Problem -- A.3 NP-Completeness of the Knapsack Problem -- A.4 NP-Completeness of Other | |
Knapsack Problems -- References -- Author Index | |
ISBN: | 9783540247777 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Keywords: | Mathematics , Operations research , Decision making , Computer science , Mathematical optimization , Management science , Mathematics , Optimization , Operation Research/Decision Theory , Discrete Mathematics in Computer Science , Operations Research, Management Science |
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