Détails du livre
Format
Kindle
Pages
411
Langue
Anglais
Publié
May 14, 2014
Éditeur
Elsevier Science & Technology
ISBN-10
008087522X
ISBN-13
9780080875224
Description
No one working in duality should be without a copy of Convex Analysis and Variational Problems. This book contains different developments of infinite dimensional convex programming in the context of convex analysis, including duality, minmax and Lagrangians, and convexification of nonconvex optimization problems in the calculus of variations (infinite dimension). It also includes the theory of convex duality applied to partial differential equations; no other reference presents this in a systematic way. The minmax theorems contained in this book have many useful applications, in particular the robust control of partial differential equations in finite time horizon. First published in English in 1976, this SIAM Classics in Applied Mathematics edition contains the original text along with a new preface and some additional references.
Audience
Practitioners of duality in such fields as mathematical economy, nonlinear programming, continuum mechanics (solids and fluids), mixed finite elements, and control theory will find this text indispensable. Analysts interested in partial differential equations will also find it useful.
About the Authors
Ivar Ekeland is the Director of the Institute of Finance of the University of Paris at Dauphine and a member of the Norwegian Academy of Sciences. In 1996, he won the Grand Prix de l'Acad
Audience
Practitioners of duality in such fields as mathematical economy, nonlinear programming, continuum mechanics (solids and fluids), mixed finite elements, and control theory will find this text indispensable. Analysts interested in partial differential equations will also find it useful.
About the Authors
Ivar Ekeland is the Director of the Institute of Finance of the University of Paris at Dauphine and a member of the Norwegian Academy of Sciences. In 1996, he won the Grand Prix de l'Acad
Genres
Affaires & Économie
Classiques