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 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 ...
International audienceModern signal processing (SP) methods rely very heavily on probability and sta...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
A general method for parallel and vector numerical solutions of stochastic dynamic programming probl...
Solutions techniques for stochastic programs are reviewed. Particular emphasis is placed on those me...
This paper contains a detailed description of the Stochastic Nonlinear Programming System (SNLP) int...
The purpose of this conference, which was attended by 240 scientists from 20 countries, was to surve...
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In...
Numerical methods for stochastic differential equations, including Taylor expansion approximations, ...
Stochastic programming is an optimization approach taking into account uncertainties in the system m...
Two topics are addressed. The first refers to the numerical computation of integrals and expected va...
This paper contains most of the documentation for a collection of routines designed to solve problem...
A finitely convergent non-simplex method for large scale structured linear programming problems aris...
We present a numerical method for finite-horizon stochastic optimal control models. We derive a stoc...
This book addresses stochastic optimization procedures in a broad manner. The first part offers an o...
Applying method of stochastic algorithms to solution of discrete optimization problem
International audienceModern signal processing (SP) methods rely very heavily on probability and sta...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
A general method for parallel and vector numerical solutions of stochastic dynamic programming probl...
Solutions techniques for stochastic programs are reviewed. Particular emphasis is placed on those me...
This paper contains a detailed description of the Stochastic Nonlinear Programming System (SNLP) int...
The purpose of this conference, which was attended by 240 scientists from 20 countries, was to surve...
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In...
Numerical methods for stochastic differential equations, including Taylor expansion approximations, ...
Stochastic programming is an optimization approach taking into account uncertainties in the system m...
Two topics are addressed. The first refers to the numerical computation of integrals and expected va...
This paper contains most of the documentation for a collection of routines designed to solve problem...
A finitely convergent non-simplex method for large scale structured linear programming problems aris...
We present a numerical method for finite-horizon stochastic optimal control models. We derive a stoc...
This book addresses stochastic optimization procedures in a broad manner. The first part offers an o...
Applying method of stochastic algorithms to solution of discrete optimization problem
International audienceModern signal processing (SP) methods rely very heavily on probability and sta...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
A general method for parallel and vector numerical solutions of stochastic dynamic programming probl...