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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 Broyden–Fletcher–Goldfarb–Shanno algorithm ： ウィキペディア英語版 Broyden–Fletcher–Goldfarb–Shanno algorithm In numerical optimization, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization problems. The BFGS method approximates Newton's method, a class of hill-climbing optimization techniques that seeks a stationary point of a (preferably twice continuously differentiable) function. For such problems, a necessary condition for optimality is that the gradient be zero. Newton's method and the BFGS methods are not guaranteed to converge unless the function has a quadratic Taylor expansion near an optimum. These methods use both the first and second derivatives of the function. However, BFGS has proven to have good performance even for non-smooth optimizations. In quasi-Newton methods, the Hessian matrix of second derivatives doesn't need to be evaluated directly. Instead, the Hessian matrix is approximated using rank-one updates specified by gradient evaluations (or approximate gradient evaluations). Quasi-Newton methods are generalizations of the secant method to find the root of the first derivative for multidimensional problems. In multi-dimensional problems, the secant equation does not specify a unique solution, and quasi-Newton methods differ in how they constrain the solution. The BFGS method is one of the most popular members of this class.〔, page 24〕 Also in common use is L-BFGS, which is a limited-memory version of BFGS that is particularly suited to problems with very large numbers of variables (e.g., >1000). The BFGS-B variant handles simple box constraints. == Rationale == The search direction p''k'' at stage ''k'' is given by the solution of the analogue of the Newton equation :$B\_k\; \backslash mathbf\_k\; =\; -\; \backslash nabla\; f(\backslash mathbf\_k)$ where $B\_k$ is an approximation to the Hessian matrix which is updated iteratively at each stage, and $\backslash nabla\; f(\backslash mathbf\_k)$ is the gradient of the function evaluated at x''k''. A line search in the direction p''k'' is then used to find the next point x''k''+1. Instead of requiring the full Hessian matrix at the point x''k''+1 to be computed as ''B''''k''+1, the approximate Hessian at stage ''k'' is updated by the addition of two matrices. :$B\_=B\_k+U\_k+V\_k\backslash ,\backslash !$ Both ''Uk'' and ''Vk'' are symmetric rank-one matrices but have different (matrix) bases. The symmetric rank one assumption here means that we may write :$C=\backslash mathbf\backslash mathbf^\backslash mathrm$ So equivalently, ''Uk'' and ''Vk'' construct a rank-two update matrix which is robust against the scale problem often suffered in the gradient descent searching (''e.g.'', in Broyden's method). The quasi-Newton condition imposed on this update is :$B\_\; (\backslash mathbf\_-\backslash mathbf\_k\; )\; =\; \backslash nabla\; f(\backslash mathbf\_)\; -\backslash nabla\; f(\backslash mathbf\_k\; ).$ 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「Broyden–Fletcher–Goldfarb–Shanno algorithm」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.