Handbook of Combinatorial Optimization pp 479-532 | Cite as

# Reformulation-Linearization Techniques for Discrete Optimization Problems

## Abstract

Discrete and continuous nonconvex programming problems arise in a host of practical applications in the context of production, location-allocation, distribution, economics and game theory, process design, and engineering design situations. Several recent advances have been made in the development of branch-and-cut algorithms for discrete optimization problems and in polyhedral outer-approximation methods for continuous nonconvex programming problems. At the heart of these approaches is a sequence of linear programming problems that drive the solution process. The success of such algorithms is strongly tied in with the strength or tightness of the linear programming representations employed.

## Keywords

Convex Hull Discrete Optimization Valid Inequality Quadratic Assignment Problem Discrete Optimization Problem## Preview

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## References

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