Stochastic programming 84

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Sparse markowitz portfolio selection by using stochastic linear complementarity approach. Behrouz Kheirfam. A weighted-path-following method for symmetric cone linear complementarity problems.


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  4. A class of smoothing SAA methods for a stochastic linear complementarity problem.
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The linear convergence of a derivative-free descent method for nonlinear complementarity problems. Neural network smoothing approximation method for stochastic variational inequality problems. A smoothing Broyden-like method for polyhedral cone constrained eigenvalue problem. Zheng-Hai Huang , Jie Sun. A smoothing Newton algorithm for mathematical programs with complementarity constraints.

Chunlin Hao , Xinwei Liu. A trust-region filter-SQP method for mathematical programs with linear complementarity constraints. Mingzheng Wang , M. Montaz Ali , Guihua Lin. Sample average approximation method for stochastic complementarity problems with applications to supply chain supernetworks. On the continuous dependence of solutions to a fractional Dirichlet problem.

SIAM Journal on Optimization

The case of saddle points. On the convergence properties of a smoothing approach for mathematical programs with symmetric cone complementarity constraints. Gabriella Pinzari. Announcement of result.

Table of contents

Electronic Research Announcements , , A potential reduction method for tensor complementarity problems. Smoothing Newton algorithm based on a regularized one-parametric class of smoothing functions for generalized complementarity problems over symmetric cones.

A class of smoothing SAA methods for a stochastic linear complementarity problem

Shaolin Ji , Xiaole Xue. A stochastic maximum principle for linear quadratic problem with nonconvex control domain. American Institute of Mathematical Sciences. Previous Article Identification of water quality model parameters using artificial bee colony algorithm.

An efficient algorithm for convex quadratic semi-definite optimization. A class of smoothing sample average approximation SAA methods is proposed for solving a stochastic linear complementarity problem, where the underlying function is the expected value of stochastic function. Existence and convergence results to the proposed methods are provided and some numerical results are reported to show the efficiency of the methods proposed.

Keywords: stochastic linear complementarity problem. A class of smoothing SAA methods for a stochastic linear complementarity problem. References: [1] B. Google Scholar [2] C. Google Scholar [3] C. Google Scholar [4] X. Google Scholar [5] X. Google Scholar [6] F. Google Scholar [7] G. Google Scholar [8] H.

Lecture 25 Stochastic Optimization

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DML-CZ - Czech Digital Mathematics Library: Prediction in stochastic linear programming

Google Scholar [16] H. Ruszczynski, eds. Qualitative stability of convex programs with probabilistic constraints, in: Lect. Notes in Economics and Mathematical Systems, vol. Nguyen, J.


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