e-books in Applied Mathematics: Optimization category
by K.J.H. Law, A.M. Stuart, K.C. Zygalakis - arXiv.org , 2015
This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation. Authors develop a framework in which a Bayesian formulation of the problem provides the bedrock for the derivation and analysis of algorithms.
by Marius Durea, Radu Strugariu - De Gruyter Open , 2014
Starting with the case of differentiable data and the classical results on constrained optimization problems, continuing with the topic of nonsmooth objects involved in optimization, the book concentrates on both theoretical and practical aspects.
by Alexander Bolonkin - viXra.org , 2017
This book describes new method of optimization (''Method of Deformation of Functional'') that has the advantages at greater generality and flexibility as well as the ability to solve complex problems which other methods cannot solve.
by Ozgur Baskan (ed.) - InTech , 2016
This book covers state-of-the-art optimization methods and their applications in wide range especially for researchers and practitioners who wish to improve their knowledge in this field. It covers applications in engineering and various other areas.
by Dariush Khezrimotlagh - arXiv , 2016
I wrote this book as a self-teaching tool to assist every teacher, student, mathematician or non-mathematician, and to support their understanding of the elementary concepts on assessing the performance of a set of homogenous firms ...
by Bram L. Gorissen, Ihsan Yanıkoğlu, Dick den Hertog - arXiv , 2015
The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do's and don'ts for using it in practice.
by Katta G. Murty - Springer , 2010
This is a Junior level book on some versatile optimization models for decision making in common use. The aim of this book is to develop skills in mathematical modeling, and in algorithms and computational methods to solve and analyze these models.
by Jim Burke - University of Washington , 2012
These are notes for an introductory course in linear programming. The four basic components of the course are modeling, solution methodology, duality theory, and sensitivity analysis. We focus on the simplex algorithm due to George Dantzig.
by Guido Schaefer - Utrecht University , 2012
From the table of contents: Preliminaries (Optimization Problems); Minimum Spanning Trees; Matroids; Shortest Paths; Maximum Flows; Minimum Cost Flows; Matchings; Integrality of Polyhedra; Complexity Theory; Approximation Algorithms.
by A. Ben-Tal, L. El Ghaoui, A. Nemirovski - Princeton University Press , 2009
Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of this relatively new approach to optimization.
by John Cea - Tata Institute of Fundamental Research , 1978
Contents: Differential Calculus in Normed Linear Spaces; Minimization of Functionals; Minimization Without Constraints; Minimization with Constraints; Duality and Its Applications; Elements of the Theory of Control and Elements of Optimal Design.
by C.T. Kelley - Society for Industrial Mathematics , 1987
This book presents a carefully selected group of methods for unconstrained and bound constrained optimization problems and analyzes them in depth both theoretically and algorithmically. It focuses on clarity in algorithmic description and analysis.
by Bruce A. McCarl, Thomas H. Spreen - Texas A&M University , 2011
This book is intended to both serve as a reference guide and a text for a course on Applied Mathematical Programming. The text concentrates upon conceptual issues, problem formulation, computerized problem solution, and results interpretation.
by Thomas S. Ferguson - UCLA , 2008
From the table of contents: Stopping Rule Problems; Finite Horizon Problems; The Existence of Optimal Rules; Applications. Markov Models; Monotone Stopping Rule Problems; Maximizing the Rate of Return; Bandit Problems; Solutions to the Exercises.
by D. P. Williamson, D. B. Shmoys - Cambridge University Press , 2010
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. It is organized around techniques for designing approximation algorithms, including greedy and local search algorithms.
by S. Bradley, A. Hax, T. Magnanti - Addison-Wesley , 1977
This book shows you how to model a wide array of problems. Covered are topics such as linear programming, duality theory, sensitivity analysis, network/dynamic programming, integer programming, non-linear programming, and my favorite, etc.
by Katta G. Murty , 1997
This book provides an in-depth and clear treatment of all the important practical, technical, computational, geometric, and mathematical aspects of the Linear Complementarity Problem, Quadratic Programming, and their various applications.
by U. Helmke, J. B. Moore - Springer , 1996
Aimed at mathematics and engineering graduate students and researchers in the areas of optimization, dynamical systems, control systems, signal processing, and linear algebra. The problems solved are those of linear algebra and linear systems theory.
by Pravin Varaiya - Van Nostrand , 1972
The author presents the main concepts mathematical programming and optimal control to students having diverse technical backgrounds. A reasonable knowledge of advanced calculus, linear algebra, and linear differential equations is required.
by Ian Craw - University of Aberdeen , 2002
The book describes the simplex algorithm and shows how it can be used to solve real problems. It shows how previous results in linear algebra give a framework for understanding the simplex algorithm and describes other optimization algorithms.
by P.-A. Absil, R. Mahony, R. Sepulchre - Princeton University Press , 2007
Many science and engineering problems can be rephrased as optimization problems on matrix search spaces endowed with a manifold structure. This book shows how to exploit the structure of such problems to develop efficient numerical algorithms.
by Stephen Boyd, Lieven Vandenberghe - Cambridge University Press , 2004
A comprehensive introduction to the subject for students and practitioners in engineering, computer science, mathematics, statistics, finance, etc. The book shows in detail how optimization problems can be solved numerically with great efficiency.