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eBook Convex Analysis and Global Optimization (Nonconvex Optimization and Its Applications) download

by Hoang Tuy

eBook Convex Analysis and Global Optimization (Nonconvex Optimization and Its Applications) download ISBN: 0792348184
Author: Hoang Tuy
Publisher: Springer; 1998 edition (January 31, 1998)
Language: English
Pages: 340
ePub: 1149 kb
Fb2: 1805 kb
Rating: 4.8
Other formats: txt docx doc lit
Category: Work and Money
Subcategory: Management and Leadership

Convex analysis and nonlinear optimization: Theory and examples.

Convex analysis and nonlinear optimization: Theory and examples. Convex analysis and nonlinear optimization: Theory and examples. Convex Optimization, Solutions Manual.

Springer Optimization and Its Applications. Convex Analysis and Global Optimization.

Convex Analysis and Optimization . by Dimitri P. Bertsekas with Angelia Nedic and Asuman E. Ozdaglar. The book's treatment of convexity theory is rigorous, insightful, and quite comprehensive, with all major aspects of the subject receiving substantial treatment. The mathematical development is ambitiously novel and uses a handful of unifying principles that can be easily visualized and understood. provides a geometric, highly visual treatment of convex and nonconvex optimization problems, including existence of solutions, optimality conditions, Lagrange multipliers, and duality.

This book presents state-of-the-art results and methodologies in modern global optimization, and .

Discussion of modern approaches to minimax, fixed point, and equilibrium theorems, and to nonconvex optimization

5 Applications to Non-convex Optimization.

5 Applications to Non-convex Optimization. A Proof of thm:main: Integration error of discretized diffusions. Central to our approach are new explicit Stein factor bounds on the solutions of Poisson equations. We complement these results with improved optimization guar-antees for targets other than the standard Gibbs measure. Consider the unconstrained and possibly non-convex optimization problem minimize f (x). x∈Rd.

Since, however, convexity in nonconvex optimization problems is present only partially or in the other way, new . H. Tuy, Convex Analysis and Global Optimization, Springer Optimization and Its Applications 110, DOI 1. 6 1 4 1 Convex Sets

Since, however, convexity in nonconvex optimization problems is present only partially or in the other way, new concepts have to be introduced and new questions have to be answered. Therefore, a new chapter on dc functions and dc sets is added to the traditional material of convex analysis. Part I of this book is an introduction to convex analysis interpreted in this broad sense as an indispensable tool for global optimization. 6 1 4 1 Convex Sets. fx D .  a/j  2 Rg: A.  1/-dimensional affine set is called a hyperplane, or. a plane for short.

Автор: Tuy Название: Convex Analysis and Global Optimization Издательство: Springer .

Электронная книга "Convex Analysis and Global Optimization", Hoang Tuy. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Convex Analysis and Global Optimization" для чтения в офлайн-режиме.

This book presents state-of-the-art results and methodologies in modern global optimization, and has been a. .

Global optimization of a non-convex objective function often appears in large-scale machine-learning and artificial intelligence applications. Recently, consensus-based optimization (in short CBO) methods have been introduced as one of the gradient-free optimization methods.

Due to the general complementary convex structure underlying most nonconvex optimization problems encountered in applications, convex analysis plays an essential role in the development of global optimization methods. This book develops a coherent and rigorous theory of deterministic global optimization from this point of view. Part I constitutes an introduction to convex analysis, with an emphasis on concepts, properties and results particularly needed for global optimization, including those pertaining to the complementary convex structure. Part II presents the foundation and application of global search principles such as partitioning and cutting, outer and inner approximation, and decomposition to general global optimization problems and to problems with a low-rank nonconvex structure as well as quadratic problems. Much new material is offered, aside from a rigorous mathematical development. Audience: The book is written as a text for graduate students in engineering, mathematics, operations research, computer science and other disciplines dealing with optimization theory. It is also addressed to all scientists in various fields who are interested in mathematical optimization.