Parallel Computing in Optimization - Applied Optimization 1997 edition

Marc Notes: Includes bibliographical references and index. Table of Contents: Preface. 1. Models for Parallel Algorithm Design: An Introduction; A. Ferreira, M. Morvan. 2. Parallel Algorithms and Complexity; M. Furer. 3. A Programmer's View of Parallel Computers; T. Sorevik. 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."Publisher Marketing: During the last three decades, breakthroughs in computer technology have made an impact on optimization. In particular, parallel computing has made it possible to solve larger and computationally more difficult problems.

Contributor Bio:  Pardalos, Panos M Panos M. Pardalos is one of the leading experts in global optimization and control theory. V. Yatsenko's research is connected with control of bilinear systems, nonlinear estimation, control of quantum systems, and globabl optimization problems. Both Pardalos and Yatsenko have authored numerous publications including books and well-known scientific journals.