文档介绍：Preface
The area of stochastic programming was created in the middle of the last
century, following fundamental achievements in linear and nonlinear
programming. While it has been quickly realized that the presence of
uncertainty in optimization models creates a need for new problem formul-
ations, many years have passed until the basic stochastic programming models
have been formulated and analyzed. Today, stochastic programming theory
offers a variety of models to address the presence of random data in
optimization problems: chance-constrained models, two- and multi-stage
models, models involving risk measures. New problem formulations appear
almost every year and this variety is one of the strengths of the field.
Stochastic programming can be quite involved, starting with sophisticated
modeling and is based on advanced mathematical tools such as nonsmooth
calculus, abstract optimization, probability theory and statistical techniques.
One of the objectives of this Handbook is to bring these techniques together
and to show how they can be used to analyze and solve stochastic program-
ming models.
Because of the inherent difficulty of stochastic optimization problems, it
took a long time until efficient solution methods have been developed. In the
last two decades a dramatic change in our abilities to solve stochastic
programming problems took place. It is partially due to the progress in large
scale linear and nonlinear programming, in nonsmooth optimization and
integer programming, but mainly it follows the development of techniques
exploiting specific properties of stochastic programming problems. Computa-
tional advances are also due to modern parallel processing technology.
Nowadays we can solve stochastic optimization problems involving tens of
millions of variables and constraints.
Our intention was to bring together leading experts in the most
important sub-fields of stochastic programming to present a rigorous
overview of basic mode