Convex Optimization In Python : Python Implementations For Examples in Textbook, Convex Optimization
by Edwin Jiang
English | 2022 | ISBN: NA | 619 Pages | PDF | 13.2 MB

English | 2022 | ISBN: 0262046989 | 232 pages | True EPUB | 14.49 MB
New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.

Convex Analysis and Beyond Volume I: Basic Theory by Boris S. Mordukhovich, Nguyen Mau Nam
English | EPUB | 2022 | 597 Pages | ISBN : 303094784X | 54.3 MB

Analysis II: Convex Analysis and Approximation Theory by R. V. Gamkrelidze
English | PDF | 1990 | 262 Pages | ISBN : 3642647685 | 30.1 MB
Intended for a wide range of readers, this book covers the main ideas of convex analysis and approximation theory. The author discusses the sources of these two trends in mathematical analysis, develops the main concepts and results, and mentions some beautiful theorems. The relationship of convex analysis to optimization problems, to the calculus of variations, to optimal control and to geometry is considered, and the evolution of the ideas underlying approximation theory, from its origins to the present day, is discussed. The book is addressed both to students who want to acquaint themselves with these trends and to lecturers in mathematical analysis, optimization and numerical methods, as well as to researchers in these fields who would like to tackle the topic as a whole and seek inspiration for its further development.

Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms by Roman G. Strongin, Yaroslav D. Sergeyev
English | PDF | 2000 | 717 Pages | ISBN : 0792364902 | 54.6 MB
Everything should be made as simple as possible, but not simpler. (Albert Einstein, Readers Digest, 1977) The modern practice of creating technical systems and technological processes of high effi.ciency besides the employment of new principles, new materials, new physical effects and other new solutions ( which is very traditional and plays the key role in the selection of the general structure of the object to be designed) also includes the choice of the best combination for the set of parameters (geometrical sizes, electrical and strength characteristics, etc.) concretizing this general structure, because the Variation of these parameters ( with the structure or linkage being already set defined) can essentially affect the objective performance indexes.

Dimitri P. Bertsekas, "Convex Optimization Theory"
English | 2009 | pages: 256 | ISBN: 1886529310 | DJVU | 3,9 mb
An insightful, concise, and rigorous treatment of the basic theory of convex sets and functions in finite dimensions, and the analytical/geometrical foundations of convex optimization and duality theory. Convexity theory is first developed in a simple accessible manner, using easily visualized proofs. Then the focus shifts to a transparent geometrical line of analysis to develop the fundamental duality between descriptions of convex sets and functions in terms of points and in terms of hyperplanes. Finally, convexity theory and abstract duality are applied to problems of constrained optimization, Fenchel and conic duality, and game theory to develop the sharpest possible duality results within a highly visual geometric framework. The book may be used as a text for a theoretical convex optimization course; the author has taught several variants of such a course at MIT and elsewhere over the last ten years. It may also be used as a supplementary source for nonlinear programming classes, and as a theoretical foundation for classes focused on convex optimization models (rather than theory). It is an ideal companion to the books Convex Optimization Algorithms, and Nonlinear Programming by the same author.

Chuanming Zong, "The Cube-A Window to Convex and Discrete Geometry"
English | 2006 | pages: 185 | ISBN: 0521855357 | PDF | 0,7 mb
Eight topics about the unit cubes are introduced within this textbook: cross sections, projections, inscribed simplices, triangulations, 0/1 polytopes, Minkowski's conjecture, Furtwangler's conjecture, and Keller's conjecture. In particular Chuanming Zong demonstrates how deep analysis like log concave measure and the Brascamp-Lieb inequality can deal with the cross section problem, how Hyperbolic Geometry helps with the triangulation problem, how group rings can deal with Minkowski's conjecture and Furtwangler's conjecture, and how Graph Theory handles Keller's conjecture.

Convex Variational Problems: Linear, Nearly Linear and Anisotropic Growth Conditions by Michael Bildhauer
English | PDF | 2003 | 220 Pages | ISBN : 3540402985 | 2.7 MB
The author emphasizes a non-uniform ellipticity condition as the main approach to regularity theory for solutions of convex variational problems with different types of non-standard growth conditions.

Statistical Inference Via Convex Optimization by Anatoli Juditsky PDF | 18.76 MB
English | 656 Pages

Title: Statistical Inference via Convex Optimization (Princeton Series in Applied Mathematics Book 65)
Author: Anatoli Juditsky
Year: 2020

English | 2021 | ISBN: 1108482023 | 342 pages | True PDF | 4.46 MB
In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.