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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" ."
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.
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