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On the theory of conjugate functions
On minimax decision rule in stochastic linear programming
12 other sections not shown
algorithm assume Automation Institute Hungarian basis binary relation block block-angular linear problems calculate coefficients column Computer and Automation cone conjugate function consider Constraints Algorithm convex function convex set convex with respect corresponding coupling constraints coupling variables Dantzig decision defined Definition denote distribution dual efficient vectors element extremal algebra feasible solution feeder arcs finite formulation GBBF given go to step graph half-spaces Hence hyperplane inequalities Institute Hungarian Academy Integer Programming inverse isotonic isotonic regression iteration lemma Let F linear programming lower bound Math mathematical programming matrix maximize méthode des centres Minimax minimize Mj(P Nonlinear Programming objective function obtain Operations Research optimal solution optimization problems parameter dependence Prikopa primal procedure properties pseudoelementary quadratic quadratic programming random variables relation Sciences 1014 Budapest set F Simplex Method solve Status Label Status stochastic programming stochastic programming model strategy structure Theorem