Sequential Randomized Algorithms for Convex Optimization in the Presence of Uncertainty
In this paper, we propose new sequential randomized algorithms for convex optimization problems in the presence of uncertainty. A rigorous analysis of the theoretical properties of the solutions obtained by these algorithms, for full constraint satisfaction and partial constraint satisfaction, respe...
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| Veröffentlicht in: | arXiv.org 2015-09 |
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| creator | Chamanbaz, Mohammadreza Dabbene, Fabrizio Tempo, Roberto Venkatakrishnan Venkataramanan Qing-Guo, Wang |
| description | In this paper, we propose new sequential randomized algorithms for convex optimization problems in the presence of uncertainty. A rigorous analysis of the theoretical properties of the solutions obtained by these algorithms, for full constraint satisfaction and partial constraint satisfaction, respectively, is given. The proposed methods allow to enlarge the applicability of the existing randomized methods to real-world applications involving a large number of design variables. Since the proposed approach does not provide a priori bounds on the sample complexity, extensive numerical simulations, dealing with an application to hard-disk drive servo design, are provided. These simulations testify the goodness of the proposed solution. |
| doi_str_mv | 10.48550/arxiv.1304.2222 |
| format | Article |
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| subjects | Algorithms Computational geometry Computer Science - Systems and Control Computer simulation Convex analysis Convexity Disk drives Mathematics - Optimization and Control Optimization Randomization Randomized algorithms Uncertainty analysis |
| title | Sequential Randomized Algorithms for Convex Optimization in the Presence of Uncertainty |
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