Parallel Computing In Optimization

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 problems.
The book covers recent developments in novel programming and algorithmic aspects of parallel computing as well as technical advances in parallel optimization. Each contribution is essentially expository in nature, but of scholarly treatment. In addition, each chapter includes a collection of carefully selected problems.
The first two chapters discuss theoretical models for parallel algorithm design and their complexity. The next chapter gives the perspective of the programmer practicing parallel algorithm development on real world platforms. Solving systems of linear equations efficiently is of great importance not only because they arise in many scientific and engineering applications but also because algorithms for solving many optimization problems need to call system solvers and subroutines (chapters four and five). Chapters six through thirteen are dedicated to optimization problems and methods. They include parallel algorithms for network problems, parallel branch and bound techniques, parallel heuristics for discrete and continuous problems, decomposition methods, parallel algorithms for variational inequality problems, parallel algorithms for shastic programming, and neural networks.
Audience: Parallel Computing in Optimization is addressed not only to researchers of mathematical programming, but to all scientists in various disciplines who use optimization methods in parallel and multiprocessing environments to model and solve problems.

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 problems.
The book covers recent developments in novel programming and algorithmic aspects of parallel computing as well as technical advances in parallel optimization. Each contribution is essentially expository in nature, but of scholarly treatment. In addition, each chapter includes a collection of carefully selected problems.
The first two chapters discuss theoretical models for parallel algorithm design and their complexity. The next chapter gives the perspective of the programmer practicing parallel algorithm development on real world platforms. Solving systems of linear equations efficiently is of great importance not only because they arise in many scientific and engineering applications but also because algorithms for solving many optimization problems need to call system solvers and subroutines (chapters four and five). Chapters six through thirteen are dedicated to optimization problems and methods. They include parallel algorithms for network problems, parallel branch and bound techniques, parallel heuristics for discrete and continuous problems, decomposition methods, parallel algorithms for variational inequality problems, parallel algorithms for stochastic programming, and neural networks.
Audience: Parallel Computing in Optimization is addressed not only to researchers of mathematical programming, but to all scientists in various disciplines who use optimization methods in parallel and multiprocessing environments to model and solve problems.

Booknews

Lecture notes from a Nordic Summer School held in Link<:o>ping, Sweden in August 1995 are augmented by a few papers invited to round out the coverage. Introduces graduate and advanced undergraduate students to novel programming and algorithmic aspects of parallel computing and technical advances in parallel optimization. The 13 papers consider models for parallel algorithm design, a programmer's perspective, scalable parallel algorithms for sparse linear systems, network problems, heuristics for combinatorial search, cost-approximation algorithms for differentiable optimization, computing variational inequalities and projected dynamical systems, large-scale stochastic programming, deterministic and stochastic logarithmic barrier function methods for neural network training, and other topics. Annotation c. by Book News, Inc., Portland, Or.