Iterative Method for Solving the Linear Feasibility Problem

Many optimization problems reduce to the solution of a system of linear inequalities (SLI). Some solution methods use relaxed, averaged projections. Others invoke surrogate constraints (typically stemming from aggregation). This paper proposes a blend of these two approaches. A novelty comes from introducing as surrogate constraint a halfspace defined by differences of algorithmic iterates. The first iteration is identical to surrogate constraints methods. In next iterations, for a given approximation , besides the violated constraints in , we also take into consideration the surrogate inequality, which we have obtained in the previous iteration. The motivation for this research comes from the recent work of Scolnik et al. (Appl. Numer. Math. 41, 499–513, 2002), who studied some projection methods for a system of linear equations. The author thanks Professor Andrzej Cegielski for suggesting the problem and many helpful discussions during the preparation of the paper.