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ALGORITHM ALGORITHM 0UMP ALGORITHM PATH analysis applied assume assumptions Attouch Banach space Berlin compute conjugate gradient method connected component consider constraints continuous convergence convex functions convex polynomials convex programming convex sets defined denote differentiable DMUs Ekeland's variational principle exists f and g feasible set finite functions f g.c. point global GOTO gradient Guddat Hence holds index set iteration Jongen jump Klatte Kummer Lagrange multiplier Lemma LICQ linearly independent Lipschitz lower semicontinuous Mangasarian-Fromovitz CQ mapping Math Mathematical Programming matrix metric MFCQ minimizer neighborhood nonlinear programs notation obtain optimal value optimization problems paper Parametric Optimization perturbations point of Type polynomial Proof properties Proposition prove real number relaxation method Remark robot satisfied semi-infinite solving space stability result stationary indices stationary point stationary solution step strongly stable subset Suppose Tammer Theorem 2.2 tion turning point vector