Updates for this new edition include:
· Discussion of modern approaches to minimax, fixed point, and equilibrium theorems, and to nonconvex optimization;
· Increased focus on dealing more efficiently with ill-posed problems of global optimization, particularly those with hard constraints;
· Important discussions of decomposition methods for specially structured problems;
· A complete revision of the chapter on nonconvex quadratic programming, in order to encompass the advances made in quadratic optimization since publication of the first edition.
· Additionally, this new edition contains entirely new chapters devoted to monotonic optimization, polynomial optimization and optimization under equilibrium constraints, including bilevel programming, multiobjective programming, and optimization with variational inequality constraint.
From the reviews of the first edition:
The book gives a good review of the topic. ...The text is carefully constructed and well written, the exposition is clear. It leaves a remarkable impression of the concepts, tools and techniques in global optimization. It might also be used as a basisand guideline for lectures on this subject. Students as well as professionals will profitably read and use it.-Mathematical Methods of Operations Research, 49:3 (1999)
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
"This reviewer believes that the book can be recommended not only to researchers but also to graduate students ... and even practioners, who can identify problems arising from various fields among those dealt with in the book." (Sorin-Mihai Grad,Mathematical Reviews, June, 2017)
"The book is a well-prepared exposition of the state-of-the-art of the theory and algorithms in the area of modern global optimization. ... a good choice if one needs a textbook for graduate or PhD course. It would be also suitable for engineers and other practitionners that would like to better understand the algorithms that they use." (Marcin Anholcer, zbMATH 1362.90001, 2017)
Mathematical Methods of Operations Research, 49:3 (1999)