The next few years will be exciting as prototype universal quantum processors emerge,
enabling the implementation of a wider variety of algorithms. Of particular interest are …

This chapter surveys how semidefinite programming can be used for finding good
approximative solutions to hard combinatorial optimization problems. The chapter begins …

Boolean functions are perhaps the most basic objects of study in theoretical computer
science. They also arise in other areas of mathematics, including combinatorics, statistical …

“Satisfiability (SAT) related topics have attracted researchers from various disciplines: logic,
applied areas such as planning, scheduling, operations research and combinatorial …

This survey examines the state of the art of a variety of problems related to pseudo-Boolean
optimization, ie to the optimization of set functions represented by closed algebraic …

We give a O (√ log n)-approximation algorithm for the sparsest cut, edge expansion,
balanced separator, and graph conductance problems. This improves the O (log n) …

Written by prominent experts in the field, this monograph provides the first comprehensive,
unified presentation of the structural, algorithmic and applied aspects of the theory of …

MaxSAT solving is becoming a competitive generic approach for solving combinatorial
optimization problems, partly due to the development of new solving techniques that have …

Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms
and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical …

Semidefinite programming has been described as linear programming for the year 2000. It is
an exciting new branch of mathematical programming, due to important applications in …