Numerical Techniques for Stochastic Optimization. SPRINGER SERIES IN COMPUTATIONAL MATHEMATICS, Volume 10.

Wets, Roger J. B. and Yu Ermoliev:

ISBN 10: 0387186778 / ISBN 13: 9780387186771

Published by Springer Verlag, 1988

About this Item

571 Seiten; Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber. Es befindet sich neben dem Rückenschild lediglich ein Bibliotheksstempel im Buch; ordnungsgemäß entwidmet. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 1010.

Seller Inventory # 2132587

Bibliographic Details

• Title: Numerical Techniques for Stochastic Optimization. SPRINGER SERIES IN COMPUTATIONAL MATHEMATICS, Volume 10.
• Publisher: Springer Verlag
• Publication Date: 1988
• Language: English
• ISBN 10: 0387186778
• ISBN 13: 9780387186771
• Binding: gebundene Ausgabe
• Condition: Gut
• Seller catalogs:
  • 200 Mathematik

About this title

Synopsis

This is a comprehensive and timely overview of the numerical techniques that have been developed to solve stochastic programming problems. After a brief introduction to the field, where the accent is laid on modeling questions, the next few chapters lay out the challenges that must be met in this area. They also provide the background for the description of the computer implementations given in the third part of the book. Selected applications are described next. Some of these have directly motivated the development of the methods described in the earlier chapters. They include problems that come from facilities location, exploration investments, control of ecological systems, energy distribution and generation. Test problems are collected in the last chapter. This is the first book devoted to this subject. It comprehensively covers all major advances in the field (both Western and Russian). It is only because of the recent developments in computer technology, that we have now reached a point where our computing power matches the inherent size requirements faced in this area. The book demonstrates that a large class of stochastic programming problems are now in the range of our numerical capacities.