by Stephen Boyd, Lieven Vandenberghe
Publisher: Cambridge University Press 2004
Number of pages: 730
Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
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by C.T. Kelley - Society for Industrial Mathematics
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 U. Helmke, J. B. Moore - Springer
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 Thomas S. Ferguson - UCLA
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 Alexander Bolonkin - viXra.org
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.