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Abstract
Minimax and other game-theoretic solutions are analyzed under linear programming problems where the objective functions are stochastic due to output and price variations. By applying the complementarity principles of quadratic programming, it is shown that this class of models leads to generalized eigenvalue problems. A set of theorems is developed to characterize the optimal eigenvalues. Lines of potential applications are also indicated in brief.