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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Linear matrix inequality ： ウィキペディア英語版 Linear matrix inequality In convex optimization, a linear matrix inequality (LMI) is an expression of the form : $\backslash operatorname(y):=A\_0+y\_1A\_1+y\_2A\_2+\backslash cdots+y\_m\; A\_m\backslash geq0\backslash ,$ where * $y=(m)$ is a real vector, * $A\_0,\; A\_1,\; A\_2,\backslash dots,A\_m$ are $n\backslash times\; n$ symmetric matrices $\backslash mathbb^n$, * $B\backslash geq0$ is a generalized inequality meaning $B$ is a positive semidefinite matrix belonging to the positive semidefinite cone $\backslash mathbb\_+$ in the subspace of symmetric matrices $\backslash mathbb$. This linear matrix inequality specifies a convex constraint on ''y''. == Applications == There are efficient numerical methods to determine whether an LMI is feasible (''e.g.'', whether there exists a vector ''y'' such that LMI(''y'') ≥ 0), or to solve a convex optimization problem with LMI constraints. Many optimization problems in control theory, system identification and signal processing can be formulated using LMIs. Also LMIs find application in Polynomial Sum-Of-Squares. The prototypical primal and dual semidefinite program is a minimization of a real linear function respectively subject to the primal and dual convex cones governing this LMI. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Linear matrix inequality」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.