A randomized mirror-prox method for solving structured large-scale matrix saddle-point problems (Q2848180)
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scientific article; zbMATH DE number 6211568
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | A randomized mirror-prox method for solving structured large-scale matrix saddle-point problems |
scientific article; zbMATH DE number 6211568 |
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25 September 2013
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semidefinite programming
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eigenvalue optimization
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large-scale problems
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stochastic approximation
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mirror-prox methods
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0.94223666
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0.8789407
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0.87507695
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0.87366986
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0.87250924
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0.87118703
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0.86924314
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0.86892045
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0.8687938
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A randomized mirror-prox method for solving structured large-scale matrix saddle-point problems (English)
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The authors consider a randomized version of the so-called mirror-prox method applied to some problems related to the semidefinite programming. They first recall properties of this method for variational inequalities and matrix saddle point problems. Then they present its randomized version, which is destined for the reduction of computational efforts in case of large scale problems, give some estimates for convergence and complexity evaluation and specialize these results for the problem of minimization of the largest eigenvalue of a convex combination of given symmetric matrices. Some computational results on test examples confirming the efficiency of the randomized version are also presented.
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