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A class of stochastic linear programs termed stable stochastic linear programs defined in terms of convergence of sequences of stochastic linear programs is introduced. A sufficient regularity condition for such stability is given, slightly stronger than the necessary and sufficient condition that a stochastic linear program (SLP) have optimal value altogether. Applications of this regularity condition to Monte Carlo methods, numerical solution of the distribution problem and two-stage programming are given. (Author).

In an earlier paper, R.W. Cottle and the author proposed a special principal pivoting algorithm for a class of large, structured linear complementarity problems. The method was applied with encouraging results to data relating to the free boundary problem for infinite journal bearings. The purpose of the present paper is to compare the empirical performance of the principal pivoting method with that of two other pertinent methods. One of the latter employs LU factorizations and is adaptive in the sense that each iteration exploits the factorization associated with its predecessor. The other is a modification of the point successive overrelaxation technique. Recommendations based on the reported computational experience are made. (Author).