Approximate local search in combinatorial optimization
Local search algorithms for combinatorial optimization problems are generally of pseudopolynomial running time, and polynomial-time algorithms are not often known for finding locally optimal solutions for NP-hard optimization problems. We introduce the concept of epsilon-local optimality and show that, for every epsilon>0, an epsilon-local optimum can be identified in time polynomial in the problem size and 1/epsilon whenever the corresponding neighborhood can be searched in polynomial time. If the neighborhood can be searched in polynomial time for a delta-local optimum, a variation of our main algorithm produces a (delta+epsilon)-local optimum in time polynomial in the problem size and 1/epsilon. As a consequence, a combinatorial optimization problem has a fully polynomial-time approximation scheme if and only if the problem of determining a better neighbor in an exact neighborhood has a fully polynomial-time approximation scheme.
local search combinatorial optimization neighborhood search approximation algorithms computational complexity 0/1-integer programming