Parallel Computing in Optimization

By: Migdalas, Athanasios Migdalas, A. Migdalas (Editor)

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Hardcover | 31 May 1997

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612 Pages

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25.4 x 17.15 x 4.45

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During the last three decades, breakthroughs in computer technology have made a tremendous impact on optimization. In particular, parallel computing has made it possible to solve larger and computationally more difficult prob lems. This volume contains mainly lecture notes from a Nordic Summer School held at the Linkoping Institute of Technology, Sweden in August 1995. In order to make the book more complete, a few authors were invited to contribute chapters that were not part of the course on this first occasion. The purpose of this Nordic course in advanced studies was three-fold. One goal was to introduce the students to the new achievements in a new and very active field, bring them close to world leading researchers, and strengthen their competence in an area with internationally explosive rate of growth. A second goal was to strengthen the bonds between students from different Nordic countries, and to encourage collaboration and joint research ventures over the borders. In this respect, the course built further on the achievements of the "Nordic Network in Mathematical Programming" , which has been running during the last three years with the support ofthe Nordic Council for Advanced Studies (NorFA). The final goal was to produce literature on the particular subject, which would be available to both the participating students and to the students of the "next generation" .

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Computer Architecture & Logic Design Parallel Processing Mathematics Optimisation Game Theory Computer Programming & Software Development Business & Management Operational Research Linear Programming

Economics Economic Theory & Philosophy

Preface
Models for Parallel Algorithm Design: An Introduction	p. 1
Parallel Algorithms and Complexity	p. 27
A Programmer's View of Parallel Computers	p. 57
Scalable Parallel Algorithms for Sparse Linear Systems	p. 73
Object Oriented Mathematical Modelling and Compilation to Parallel Code	p. 99
Parallel Algorithms for Network Problems	p. 183
Parallel Branch and Bound-Principles and Personal Experiences	p. 239
Parallelized Heuristics for Combinatorial Search	p. 269
Parallel Cost Approximation Algorithms for Differentiable Optimization	p. 295
Parallel Computation of Variational Inequalities and Projected Dynamical Systems With Applications	p. 343
Parallel Algorithms for Large-Scale Stochastic Programming	p. 413
Parallel Continuous Non-Convex Optimization	p. 471
Deterministic and Stochastic Logarithmic Barrier Function Methods for Neural Network Training	p. 529
Index	p. 575
Table of Contents provided by Blackwell. All Rights Reserved.

Parallel Computing in Optimization

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Hardcover

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