A Randomized Algorithm for Robust BMI Optimization

• Wada Takayuki

  Graduate School of Information Science and Technology, Osaka University

• Fujisaki Yasumasa

  Graduate School of Information Science and Technology, Osaka University

抄録

Robust bilinear matrix inequality (BMI) optimization, which is to minimize an objective function subject to a parameter dependent BMI constraint, is considered. A recursive algorithm employing a branch-and-bound technique and randomization of a parameter is provided for solving the problem. When the algorithm finds a solution, this solution satisfies the parameter dependent constraint with a prescribed accuracy in a probabilistic sense. Furthermore, the objective function value at that solution ensures that the feasible set whose objective function value is less than this value is too small to be found.

収録刊行物

• Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications

  Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2017 (0), 111-116, 2017

  システム制御情報学会ストカスティックシステムシンポジウム

キーワード

• Materials Science (miscellaneous)

詳細情報

• CRID: 1390282680740991360
• NII論文ID: 130006192981
• DOI: 10.5687/sss.2017.111
• ISSN: 21884749
• ISSN: 21884730
• Web Site: https://www.jstage.jst.go.jp/article/sss/2017/0/2017_111/_pdf
• 本文言語コード: en
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