Posted by **nebulae** at April 13, 2014

English | ISBN: 3642407536 | 2014 | 768 pages | PDF | 6 MB

Posted by **lengen** at Nov. 15, 2016

English | October 4, 2013 | ISBN: 0412164302 | 156 Pages | PDF | 3 MB

This text is based on a course of about 16 hours lectures to students of mathematics, statistics, and/or operational research. It is intended to introduce readers to the very wide range of applicability of linear programming, covering problems of manage ment, administration, transportation and a number of other uses which are mentioned in their context. The emphasis is on numerical algorithms, which are illustrated by examples of such modest size that the solutions can be obtained using pen and paper. It is clear that these methods, if applied to larger problems, can also be carried out on automatic (electronic) computers. Commercially available computer packages are, in fact, mainly based on algorithms explained in this book.

Posted by **lengen** at Nov. 15, 2016

English | October 4, 2013 | ISBN: 0412164302 | 156 Pages | PDF | 3 MB

This text is based on a course of about 16 hours lectures to students of mathematics, statistics, and/or operational research. It is intended to introduce readers to the very wide range of applicability of linear programming, covering problems of manage ment, administration, transportation and a number of other uses which are mentioned in their context. The emphasis is on numerical algorithms, which are illustrated by examples of such modest size that the solutions can be obtained using pen and paper. It is clear that these methods, if applied to larger problems, can also be carried out on automatic (electronic) computers. Commercially available computer packages are, in fact, mainly based on algorithms explained in this book.

Posted by **ChrisRedfield** at Nov. 28, 2015

Published: 2011-07-25 | ISBN: 1441964908 | PDF | 673 pages | 5.33 MB

Posted by **arundhati** at June 14, 2015

2011 | 2nd edition | ISBN-10: 1441977287 | 448 pages | PDF | 3 MB

Posted by **tanas.olesya** at Jan. 28, 2015

English | Feb 17, 2005 | ISBN: 0387233857 | 405 Pages | PDF | 16 MB

Peter Kall and János Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization.

Posted by **step778** at May 7, 2014

1963 | pages: 634 | ISBN: 0691080003 | PDF | 25,7 mb

Posted by **zolao** at Aug. 2, 2013

Published: 2010-11-10 | ISBN: 1441977287 | PDF | 448 pages | 3 MB

This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC’s and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup.

Posted by **zolao** at July 18, 2013

Published: 2010-11-10 | ISBN: 1441977287 | PDF | 448 pages | 3 MB

This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC’s and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup.

Posted by **spiderman** at Nov. 27, 2006

Springer | ISBN 0387233857 | February 2005 | PDF | 416 Pages | 17,0 Mb

Peter Kall and János Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization. Stochastic Linear Programming: Models, Theory, and Computation is a definitive presentation and discussion of the theoretical properties of the models, the conceptual algorithmic approaches, and the computational issues relating to the implementation of these methods to solve problems that are stochastic in nature. The application area of stochastic programming includes portfolio analysis, financial optimization, energy problems, random yields in manufacturing, risk analysis, etc. In this book, models in financial optimization and risk analysis are discussed as examples, including solution methods and their implementation.