Constructive dual methods for discrete programming

Abstract

This paper gives a general theory for constructive dual methods in discrete programming. These techniques are concerned with the reduction of the feasibility set in order to obtain a dual problem which is easy to solved and has no duality gap. If a particular dual problem fails to solved the primal problem, then a stronger dual problem is constructed and the analysis continued. The relaxation approximation is made progressively tighter until, in a finite number of iterations, an optimal solution is reached. The theory presented generalises both the ‘convergent duality theory’ of Shapiro [10] and ‘the bound improving sequence algorithm (BISA)’ of Barcia [4]. An improved BISA, requiring only the solution of knapsack problems, is presented. For the case of 0–1 LP's computational experience is reported, both for problems presented in the literature as well as for randomly generated ones.