A Parametric Kernel Function Generating the best Known Iteration Bound for Large-Update Methods for CQSDO
In this paper, we propose a large-update primal-dual interior point algorithm for convex quadratic semidefiniteoptimization (CQSDO) based on a new parametric kernel function. This kernel function is a parameterized version of the kernel function introduced by M.W. Zhang (Acta Mathematica Sinica. 28:...
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| Published in: | Statistics, optimization & information computing Vol. 8; no. 4; pp. 876 - 889 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
24.09.2020
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| ISSN: | 2311-004X, 2310-5070 |
| Online Access: | Get full text |
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| Summary: | In this paper, we propose a large-update primal-dual interior point algorithm for convex quadratic semidefiniteoptimization (CQSDO) based on a new parametric kernel function. This kernel function is a parameterized version of the kernel function introduced by M.W. Zhang (Acta Mathematica Sinica. 28: 2313-2328, 2012) for CQSDO. The investigation according to it generating the best known iteration bound O for large-update methods. Thus improves the iteration bound obtained by Zhang for large-update methods. Finally, we present few numerical results to show the efficiency of the proposed algorithm. |
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| ISSN: | 2311-004X 2310-5070 |
| DOI: | 10.19139/soic-2310-5070-842 |