Nonlinear Assignment Problems (NAPs) are natural extensions of the
classic Linear Assignment Problem, and despite the efforts of many
researchers over the past three decades, they still remain some of the
hardest combinatorial optimization problems to solve exactly. The
purpose of this book is to provide in a single volume, major algorithmic
aspects and applications of NAPs as contributed by leading international
experts.
The chapters included in this book are concerned with major applications
and the latest algorithmic solution approaches for NAPs. Approximation
algorithms, polyhedral methods, semidefinite programming approaches and
heuristic procedures for NAPs are included, while applications of this
problem class in the areas of multiple-target tracking in the context of
military surveillance systems, of experimental high energy physics, and
of parallel processing are presented.
Audience: Researchers and graduate students in the areas of
combinatorial optimization, mathematical programming, operations
research, physics, and computer science