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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).

The relation between matrices with all principal minors positive and positive definite matrices is explored in the non-symmetric case. It is shown that simple rescaling of rows and columns is insufficient to transform the former into the latter. As a consequence it appears that the class of problems that can be solved by complementary pivot theory of mathematical programming has been non-trivially extended beyond the convex case represented by linear and quadratic programming and takes its place along with another important extension of Lemke and Howson for the matrix associated with bi-matrix games. (Author).