Parallel Computing in Optimization

ISBN-13: 9780792345831 / Angielski / Twarda / 1997 / 588 str.

Migdalas; A. Migdalas; P. M. Pardalos

Opis

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" .

Spis treści

Preface. 1. Models for Parallel Algorithm Design: An Introduction; A. Ferreira, M. Morvan. 2. Parallel Algorithms and Complexity; M. Fürer. 3. A Programmer's View of Parallel Computers; T. Sørevik. 4. Scalable Parallel Algorithms for Sparse Linear Systems; A. Gupta, et al. 5. Object Oriented Mathematical Modelling and Compilation to Parallel Code; N. Andersson, P. Fritzson. 6. Parallel Algorithms for Network Problems; O. Damberg, et al. 7. Parallel Branch and Bound-Principles and Personal Experiences; J. Clausen. 8. Parallelized Heuristics for Combinatorial Search; K. Holmqvist, et al. 9. Parallel Cost Approximation Algorithms for Differentiable Optimization; M. Patriksson. 10. Parallel Computation of Variational Inequalities and Projected Dynamical Systems with Applications; A. Nagurney. 11. Parallel Algorithms for Large-Scale Stochastic Programming; H. Vladimirou, S.A. Zenios. 12. Parallel Continuous Non-Convex Optimization; K. Holmqvist, et al. 13. Deterministic and Stochastic Logarithmic Barrier Function Methods for Neural Network Training; T.B. Trafalis, T.A. Tutunji. Index.