A smoothing function approach to joint chance-constrained programs (Q467479)
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scientific article; zbMATH DE number 6363607
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | A smoothing function approach to joint chance-constrained programs |
scientific article; zbMATH DE number 6363607 |
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A smoothing function approach to joint chance-constrained programs (English)
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3 November 2014
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An algorithm for stochastic joint chance-constrained optimization problems is developed using the approximation of probability and constraint functions by a difference of two convex functions. The novelty of the proposed algorithm is in the constructed approximation where the approximats are selected from a class of smoothing functions. The convergence of the approximate solution to a Karusk-Kuhn-Tucker point is proved. Several examples are provided to illustrate the performance of the proposed algorithm.
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stochastic optimization
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joint-constrained programs
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DC optimization
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sequential convex approximation
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0.94509476
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0.8886372
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0.8855326
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0.88492703
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