DECOMP: an Implementation of Dantzig-Wolfe Decomposition for Linear Programming (Paperback, 1989 ed.)

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For linear optimization models that can be formulated as linear programs with the block-angular structure, i.e. independent subproblems with coupling constraints, the Dantzig-Wolfe decomposition principle provides an elegant framework of solution algorithms as well as economic interpretation. This monograph is the complete documentation of DECOMP: a robust implementation of the Dantzig-Wolfe decomposition method in FORTRAN. The code can serve as a very convenient starting point for further investigation, both computational and economic, of parallelism in large-scale systems. It can also be used as supplemental material in a second course in linear programming, computational mathematical programming, or large-scale systems.

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Product Description

For linear optimization models that can be formulated as linear programs with the block-angular structure, i.e. independent subproblems with coupling constraints, the Dantzig-Wolfe decomposition principle provides an elegant framework of solution algorithms as well as economic interpretation. This monograph is the complete documentation of DECOMP: a robust implementation of the Dantzig-Wolfe decomposition method in FORTRAN. The code can serve as a very convenient starting point for further investigation, both computational and economic, of parallelism in large-scale systems. It can also be used as supplemental material in a second course in linear programming, computational mathematical programming, or large-scale systems.

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Product Details

General

Imprint

Springer-Verlag New York

Country of origin

United States

Series

Lecture Notes in Economics and Mathematical Systems, 338

Release date

November 1989

Availability

Expected to ship within 10 - 15 working days

First published

1989

Authors

,

Dimensions

279 x 210 x 12mm (L x W x T)

Format

Paperback - Cloth over boards

Pages

206

Edition

1989 ed.

ISBN-13

978-0-387-97154-4

Barcode

9780387971544

Categories

LSN

0-387-97154-8



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