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Just as in its 1st edition, this book starts with illustrations of the ubiquitous character of optimization, and describes numerical algorithms in a tutorial way. It covers fundamental algorithms as well as more specialized and advanced topics for unconstrained and constrained problems. Most of the algorithms are explained in a detailed manner, allowing straightforward implementation. Theoretical aspects of the approaches chosen are also addressed with care, often using minimal assumptions.

This new edition contains computational exercises in the form of case studies which help...

Ce livre est exclusivement consacre aux algorithmes numeriques d'optimisation (quasi-Newton, faisceaux, programmation quadratique successive, points interieurs); les bases theoriques (conditions d'optimalite, multiplicateurs de Lagrange) sont supposees connues.
Son but est de familiariser le lecteur avec ces algorithmes, qui sont pour la plupart bien classiques. Leur description insiste sur leur implementation numerique, ils peuvent etre programmes directement par un lecteur experimente. Le cote theorique n'est pas pour autant neglige, avec demonstration de chaque theoreme de convergence...

A presentation of general results for discussing local optimality and computation of the expansion of value function and approximate solution of optimization problems, followed by their application to various fields, from physics to economics. The book is thus an opportunity for popularizing these techniques among researchers involved in other sciences, including users of optimization in a wide sense, in mechanics, physics, statistics, finance and economics. Of use to research professionals, including graduate students at an advanced level.

The main subject of this book is perturbation analysis of continuous optimization problems. In the last two decades considerable progress has been made in that area, and it seems that it is time now to present a synthetic view of many important results that apply to various classes of problems. The model problem that is considered throughout the book is of the form (P) Min/(x) subjectto G(x) E K. xeX Here X and Y are Banach spaces, K is a closed convex subset of Y, and /: X -+ IR and G: X -+ Y are called the objective function and the constraint mapping, respectively. We also consider a...