Superlinear convergence of a predictor-corrector method for semidefinite programming without shrinking central path neighborhood (Q2762830)
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scientific article; zbMATH DE number 1689541
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Superlinear convergence of a predictor-corrector method for semidefinite programming without shrinking central path neighborhood |
scientific article; zbMATH DE number 1689541 |
Statements
13 January 2002
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semidefinite programming
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predictor-corrector
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infeasible-interior-point algorithm
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Superlinear convergence of a predictor-corrector method for semidefinite programming without shrinking central path neighborhood (English)
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The problem of consideration is to minimize a linear objective function whose unknowns have to satisfy some linear equality constraints and to be the elements of a symmetric positive semidefinite matrix. Starting from a predictor-corrector algorithm for linear programming and using previous results, the authors are proposing a new variant of an infeasible start predictor-corrector algorithm that has global linear convergence, polynomial complexity and is superlinearly convergent under strict complementarity and nondegeneracy assumptions.
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