Globally and superlinearly convergent trust-region algorithm for convex SC1-minimization problems and its application to stochastic programs |
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Authors: | H Jiang L Qi |
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Institution: | (1) School of Mathematics, University of New South Wales, Sydney, New South Wales, Australia |
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Abstract: | A function mapping from n to is called an SC1-function if it is differentiable and its derivative is semismooth. A convex SC1-minimization problem is a convex minimization problem with an SC1-objective function and linear constraints. Applications of such minimization problems include stochastic quadratic programming and minimax problems. In this paper, we present a globally and superlinearly convergent trust-region algorithm for solving such a problem. Numerical examples are given on the application of this algorithm to stochastic quadratic programs.This work was supported by the Australian Research Council.We are indebted to Dr. Xiaojun Chen for help in the computation. We are grateful to two anonymous referees for their comments and suggestions, which improved the presentation of this paper. |
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Keywords: | Trust-region algorithms global convergence superlinear convergence stochastic quadratic programs |
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