Stochastic Programming - Index of

Preface
Over the last few years, both of the authors, and also most others in the field
of stochastic programming, have said that what we need more than anything
just now is a basic textbook—a textbook that makes the area available not
only to mathematicians, but also to students and other interested parties who
cannot or will not try to approach the field via the journals. We also felt
the need to provide an appropriate text for instructors who want to include
the subject in their curriculum. It is probably not possible to write such a
book without assuming some knowledge of mathematics, but it has been our
clear goal to avoid writing a text readable only for mathematicians. We want
the book to be accessible to any quantitatively minded student in business,
economics, computer science and engineering, plus, of course, mathematics.
So what do we mean by a quantitatively minded student We assume that
the reader of this book has had a basic course in calculus, linear algebra
and probability. Although most readers will have a background in linear
programming (which replaces the need for a specific course in linear algebra),
we provide an outline of all the theory we need from linear and nonlinear
programming. We have chosen to put this material into Chapter 1, so that
the reader who is familiar with the theory can drop it, and the reader who
knows the material, but wonders about the exact definition of some term, or
who is slightly unfamiliar with our terminology, can easily check how we see
things. We hope that instructors will find enough material in Chapter 1 to
cover specific topics that may have been omitted in the standard book on
optimization used in their institution. By putting this material directly into
the running text, we have made the book more readable for those with the
minimal background. But, at the same time, we have found it best to separate
what is new in this book—stochastic programming—from more standard
material of linear and nonlinear programming.
Despite this clear goal concerning the level of mathematics, we must
admit that when treating some of the subjects, like probabilistic constraints
(Section 1.6 and Chapter 4), or particular solution methods for stochastic
programs, like stochastic decomposition (Section 3.8) or quasi-gradient