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Titlebook: Sensitivity, Stability and Parametric Analysis; Anthony V. Fiacco Book 1984Latest edition Springer-Verlag Berlin Heidelberg 1984 calculus

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楼主: 遮蔽
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Generalized equations: Solvability and regularity,egularity leads to an implicit function theorem which uses a general notion of a derivative of a multifunction. Further, quantitative stability of the (subgradient) Kuhn-Tuckerpoints to convex optimization problems is examined from the point of view how to reduce the case of general continuous perturbations to the one of linearly perturbed only.
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Directional differentiability of the optimal value function in a nonlinear programming problem, weak sufficient condition for local optimality based on the augmented Lagrangian, it is shown that the optimal value in the problem, as a function of the parameters, is directionally differentiable. The directional derivatives are expressed by a minimax formula which generalizes the one of Gol’shtein in convex programming.
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Solution point differentiability without strict complementarity in nonlinear programming,al derivative with respect to any directional perturbation always exists and is the unique solution of a system of equations and inequalities. A possible method for computing the directional derivative is also suggested.
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Stability of mixed-integer quadratic programming problems,tic objective function and the right-hand sides of the linear inequality constraints in the absence of boundedness on the feasible region and convexity on the objective function. The only hypothesis needed is the rationality of the matrices given by the left-hand sides of the constraints and the qua
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Parametric semi-infinite linear programming I. continuity of the feasible set and of the optimal vaty properties of the feasible set mapping and of the optimal value. Further, we will investigate the structure of certain parameter sets. We begin by stating some definitions and by recalling a necessary and sufficient condition that a set of feasible points is optimal. In Section 2, we derive vario
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,On imbedding and parametric optimization—A concept of a globally convergent algorithm for nonlineartion problem induced by the imbedding is analyzed. The results of this analysis are the basis for the proposed globally convergent algorithm using Robinson’s method as the local iteration method. To pass over critical points a suitable quadratic approximation is used to obtain the new index set of a
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Three methods for postoptimal analysis in integer linear programming, the optimal basic inverse. The same is not true for ILP. Optimality can only be proven through an algorithm. The possibility for postoptimal analysis is therefore dependent on the algorithm used to solve the ILP. In this paper we study methods for performing postoptimal analysis when the ILP has be
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