Non-Fiction Books:

Stochastic Decomposition

A Statistical Method for Large Scale Stochastic Linear Programming

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Stochastic Decomposition by Julia L. Higle
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Description

This work summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms. Unlike traditional deterministic algorithms, SD combines sampling approaches from the statistical literature with traditional mathematical programming constructs (for example decomposition and cutting planes). This marriage of two highly computationally oriented disciplines leads to a line of work that is most definitely driven by computational considerations. Furthermore, the use of sampled data in SD makes it extremely flexible in its ability to accommodate various representations of uncertainty, including situations in which outcomes/scenarios can only be generated by an algorithm/simulation. The authors report computational results with some of the largest stochastic programs arising in applications. These results (mathematical as well as computational) are the "tip of the iceberg". Further research will uncover extensions of SD to a wider class of problems.
Release date Australia
February 29th, 1996
Country of Publication
Netherlands
Edition
1996 ed.
Illustrations
XXIV, 222 p.
Imprint
Springer
Pages
222
Dimensions
155x235x15
ISBN-13
9780792338406
Product ID
3964751

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