CS 787 : Advanced Algorithms LP - Based Approximations


We present methods for constructing approximation algorithms to NP-hard optimization problems using linear programming (LP). We typically obtain an LP relaxation of the problem by formulating it as an integer linear program and dropping the integrality constraints. We then solve the LP relaxation exactly, and apply various rounding strategies (including randomized rounding and iterative rounding) to obtain a valid integral solution that is close to optimal. Alternately, we exploit LP duality and employ a primal-dual approach, or consider a Lagrangian relaxation to deal with difficult constraints.

Cite this paper

@inproceedings{Melkebeek2014CS7, title={CS 787 : Advanced Algorithms LP - Based Approximations}, author={Dieter van Melkebeek}, year={2014} }