Introduction to Mathematical Optimization

Introduction to Mathematical Optimization
Title Introduction to Mathematical Optimization PDF eBook
Author Matteo Fischetti
Publisher
Pages 232
Release 2019-09-12
Genre Mathematical optimization
ISBN 9781692792022

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This book is intended to be a teaching aid for students of the courses in Operations Research and Mathematical Optimization for scientific faculties. Some of the basic topics of Operations Research and Optimization are considered: Linear Programming, Integer Linear Programming, Computational Complexity, and Graph Theory. Particular emphasis is given to Integer Linear Programming, with an exposition of the most recent resolution techniques, and in particular of the branch-and-cut method. The work is accompanied by numerous examples and exercises.

Introduction to Mathematical Optimization

Introduction to Mathematical Optimization
Title Introduction to Mathematical Optimization PDF eBook
Author Xin-She Yang
Publisher
Pages 172
Release 2008
Genre Mathematics
ISBN

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This book strives to provide a balanced coverage of efficient algorithms commonly used in solving mathematical optimization problems. It covers both the convectional algorithms and modern heuristic and metaheuristic methods. Topics include gradient-based algorithms such as Newton-Raphson method, steepest descent method, Hooke-Jeeves pattern search, Lagrange multipliers, linear programming, particle swarm optimization (PSO), simulated annealing (SA), and Tabu search. Multiobjective optimization including important concepts such as Pareto optimality and utility method is also described. Three Matlab and Octave programs so as to demonstrate how PSO and SA work are provided. An example of demonstrating how to modify these programs to solve multiobjective optimization problems using recursive method is discussed.

An Introduction to Optimization

An Introduction to Optimization
Title An Introduction to Optimization PDF eBook
Author Edwin K. P. Chong
Publisher John Wiley & Sons
Pages 497
Release 2004-04-05
Genre Mathematics
ISBN 0471654000

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A modern, up-to-date introduction to optimization theory and methods This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization. Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides: * A review of the required mathematical background material * A mathematical discussion at a level accessible to MBA and business students * A treatment of both linear and nonlinear programming * An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods * A chapter on the use of descent algorithms for the training of feedforward neural networks * Exercise problems after every chapter, many new to this edition * MATLAB(r) exercises and examples * Accompanying Instructor's Solutions Manual available on request An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Practical Mathematical Optimization

Practical Mathematical Optimization
Title Practical Mathematical Optimization PDF eBook
Author Jan Snyman
Publisher Springer Science & Business Media
Pages 271
Release 2005-12-15
Genre Mathematics
ISBN 0387243496

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This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.

Practical Mathematical Optimization

Practical Mathematical Optimization
Title Practical Mathematical Optimization PDF eBook
Author Jan A Snyman
Publisher Springer
Pages 388
Release 2018-05-02
Genre Mathematics
ISBN 3319775863

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This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.

Opt Art

Opt Art
Title Opt Art PDF eBook
Author Robert Bosch (mathématicien)
Publisher Princeton University Press
Pages 200
Release 2019-11-12
Genre Art
ISBN 0691164061

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Bosch provides a lively and accessible introduction to the geometric, algebraic, and algorithmic foundations of optimization. He presents classical applications, such as the legendary Traveling Salesman Problem, and shows how to adapt them to make optimization art--opt art. art.

Mathematics of Optimization: How to do Things Faster

Mathematics of Optimization: How to do Things Faster
Title Mathematics of Optimization: How to do Things Faster PDF eBook
Author Steven J. Miller
Publisher American Mathematical Soc.
Pages 353
Release 2017-12-20
Genre Business & Economics
ISBN 1470441144

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Optimization Theory is an active area of research with numerous applications; many of the books are designed for engineering classes, and thus have an emphasis on problems from such fields. Covering much of the same material, there is less emphasis on coding and detailed applications as the intended audience is more mathematical. There are still several important problems discussed (especially scheduling problems), but there is more emphasis on theory and less on the nuts and bolts of coding. A constant theme of the text is the “why” and the “how” in the subject. Why are we able to do a calculation efficiently? How should we look at a problem? Extensive effort is made to motivate the mathematics and isolate how one can apply ideas/perspectives to a variety of problems. As many of the key algorithms in the subject require too much time or detail to analyze in a first course (such as the run-time of the Simplex Algorithm), there are numerous comparisons to simpler algorithms which students have either seen or can quickly learn (such as the Euclidean algorithm) to motivate the type of results on run-time savings.