Approximation algorithms for covering/packing integer programs

Given matrices A and B and vectors a, b, c and d, all with non-negative entries, we consider the problem of computing . We give a bicriteria-approximation algorithm that, given ε∈(0,1], finds a solution of cost O(ln(m)/ε²) times optimal, meeting the covering constraints (Ax?a) and multiplicity constraints (x?d), and satisfying Bx?(1+ε)b+β, where β is the vector of row sums β_i=∑_jB_ij. Here m denotes the number of rows of A.This gives an O(lnm)-approximation algorithm for CIP—minimum-cost covering integer programs with multiplicity constraints, i.e., the special case when there are no packing constraints Bx?b. The previous best approximation ratio has been O(ln(max_j∑_iA_ij)) since 1982. CIP contains the set cover problem as a special case, so O(lnm)-approximation is the best possible unless P=NP.