The Best Optimization Mathematics Books 2025

1. Dynamic Programming: Models and Applications [Paperback - Used]

CONDITION - USED - Pages can include limited notes and highlighting, and the copy can include "From the library of" labels or previous owner inscriptions. Accessories such as CD, codes, toys, may not be included. Designed both for those who seek an acquaintance with dynamic programming and for those wishing to become experts, this text is accessible to anyone who's taken a course in operations research. It starts with a basic introduction to sequential decision processes and proceeds to the use of dynamic programming in studying models of resource allocation. Subsequent topics include methods for approximating solutions of control problems in continuous time, production control, decision-making in the face of an uncertain future, and inventory control models. The final chapter introduces sequential decision processes that lack fixed planning horizons, and the supplementary chapters treat data structures and the basic properties of convex functions. 1982 edition. Preface to the Dover Edition.

Dynamic Programming: Models and Applications [Paperback - Used]

2. Practical Optimization, Used [Paperback]

This book is designed to help problem solvers make the best use of optimization software--i.e., to use existing methods most effectively whenever possible, and to adapt and modify techniques for particular problems if necessary. The contents of this book therefore include some topics that are essential for all those who wish to solve optimization problems. In addition, certain topics are treated that should be of special interest in most practical optimization problems. For example, advice is given to users who wish to take an active role in formulating their problems so as to enhance the chances of solving them successfully, and to users who need to understand why a certain method fails to solve their problem.

Practical Optimization, Used [Paperback]

3. Optimal Control Theory: A Course in Automatic Control Theory [Paperback - Used]

CONDITION - USED - Pages can include limited notes and highlighting, and the copy can include "From the library of" labels or previous owner inscriptions. Accessories such as CD, codes, toys, may not be included. As Entitled

Optimal Control Theory: A Course in Automatic Control Theory, Used [Paperback]

4. Mathematical Programming: Theory and Algorithms, Used [Hardcover]

This comprehensive work covers the whole field of mathematical programming, including linear programming, unconstrained and constrained nonlinear programming, nondifferentiable (or nonsmooth) optimization, integer programming, large scale systems optimization, dynamic programming, and optimization in infinite dimensions. Special emphasis is placed on unifying concepts such as point-to-set maps, saddle points and perturbations functions, duality theory and its extensions.

Mathematical Programming : Theory and Algorithms, Used [Hardcover]

5. Reinforcement Learning and Dynamic Programming Using Function Approximators [Hardcover - Used]

CONDITION - USED - Pages can include limited notes and highlighting, and the copy can include "From the library of" labels or previous owner inscriptions. Accessories such as CD, codes, toys, may not be included. From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.

Reinforcement Learning and Dynamic Programming Using Function Approximators, Used [Hardcover]

6. Foundations of Optimization, Used [Hardcover]

Foundations of Optimization by Douglass J. Wilde; Don T. Phillips; Charles S. Beightler

Foundations of Optimization, Used [Hardcover]

7. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, Used [Hardcover]

* Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.

Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, Used [Hardcover]

8. Elements of Optimization : With Applications in Economics and Business, Used [Paperback]

This book attempts to present the concepts which underlie the various optimization procedures which are commonly used. It is written primarily for those scientists such as economists, operations researchers, and en- gineers whose main tools of analysis involve optimization techniques and who possess a (not very sharp) knowledge of one or one-and-a-half year's calculus through partial differentiation and Taylor's theorem and some acquaintance with elementary vector and matrix terminology. Such a scientist is frequently confronted with expressions such as Lagrange multi- pliers, first-and second-order conditions, linear programming and activity analysis, duality, the Kuhn-Tucker conditions, and, more recently, dy- namic programming and optimal control. He or she uses or needs to use these optimization techniques, and would like to feel more comfortable with them through better understanding of their underlying mathematical concepts, but has no immediate use for a formal theorem-proof treatment which quickly abstracts to a general case of n variables and uses a style and terminology that are discouraging to people who are not mathematics majors. The emphasis of this book is on clarity and plausibility. Through examples which are worked out step by step in detail, I hope to illustrate some tools which will be useful to scientists when they apply optimization techniques to their problems. Most of the chapters may be read independently of each other-with the exception of Chapter 6, which depends on Chapter 5. For instance, the reader will find little or no difficulty in reading Chapter 8 without having read the previous chapters.

Elements of Optimization : With Applications in Economics and Business, Used [Paperback]

9. Parallel Optimization: Theory, Algorithms, and Applications, Used [Hardcover]

This book offers a unique pathway to methods of parallel optimization by introducing parallel computing ideas into both optimization theory and into some numerical algorithms for large-scale optimization problems. The three parts of the book bring together relevant theory, careful study of algorithms, and modeling of significant real world problems such as image reconstruction, radiation therapy treatment planning, financial planning, transportation and multi-commodity network flow problems, planning under uncertainty, and matrix balancing problems.

Parallel Optimization: Theory, Algorithms, and Applications, Used [Hardcover]

10. Practical Methods of Optimization [Paperback - Used]

CONDITION - USED - Pages can include limited notes and highlighting, and the copy can include "From the library of" labels or previous owner inscriptions. Accessories such as CD, codes, toys, may not be included. Fully describes optimization methods that are currently most valuable in solving real-life problems. Since optimization has applications in almost every branch of science and technology, the text emphasizes their practical aspects in conjunction with the heuristics useful in making them perform more reliably and efficiently. To this end, it presents comparative numerical studies to give readers a feel for possibile applications and to illustrate the problems in assessing evidence. Also provides theoretical background which provides insights into how methods are derived. This edition offers revised coverage of basic theory and standard techniques, with updated discussions of line search methods, Newton and quasi-Newton methods, and conjugate direction methods, as well as a comprehensive treatment of restricted step or trust region methods not commonly found in the literature. Also includes recent developments in hybrid methods for nonlinear least squares; an extended discussion of linear programming, with new methods for stable updating of LU factors; and a completely new section on network programming. Chapters include computer subroutines, worked examples, and study questions.

Practical Methods of Optimization, Used [Paperback]