Convex Optimization: Algorithms and Complexity
by Sebastien Bubeck
Publisher: arXiv.org 2015
Number of pages: 130
This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural optimization and stochastic optimization.
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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 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.
by Guido Schaefer - Utrecht University
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 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.