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 A. Ben-Tal, L. El Ghaoui, A. Nemirovski - Princeton University Press
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 Jim Burke - University of Washington
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.
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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 P.-A. Absil, R. Mahony, R. Sepulchre - Princeton University Press
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.