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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 Non-negative least squares ： ウィキペディア英語版 Non-negative least squares In mathematical optimization, the problem of non-negative least squares (NNLS) is a constrained version of the least squares problem where the coefficients are not allowed to become negative. That is, given a matrix and a (column) vector of response variables , the goal is to find〔 :$\backslash operatorname$ *_\mathbf \|\mathbf - \mathbf\|_2 subject to . Here, means that each component of the vector should be non-negative and denotes the Euclidean norm. Non-negative least squares problems turn up as subproblems in matrix decomposition, e.g. in algorithms for PARAFAC〔 and non-negative matrix/tensor factorization. The latter can be considered a generalization of NNLS.〔 Another generalization of NNLS is bounded-variable least squares (BLVS), with simultaneous upper and lower bounds . ==Quadratic programming version== The NNLS problem is equivalent to a quadratic programming problem :$\backslash operatorname$ *_\mathbf \frac \mathbf^\mathsf \mathbf\mathbf + \mathbf^\mathsf \mathbf, where = and = . This problem is convex as is positive semidefinite and the non-negativity constraints form a convex feasible set. 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「Non-negative least squares」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.