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Davidon–Fletcher–Powell formula : ウィキペディア英語版
Davidon–Fletcher–Powell formula
The Davidon–Fletcher–Powell formula (or DFP; named after William C. Davidon, Roger Fletcher, and Michael J. D. Powell) finds the solution to the secant equation that is closest to the current estimate and satisfies the curvature condition (see below). It was the first quasi-Newton method to generalize the secant method to a multidimensional problem. This update maintains the symmetry and positive definiteness of the Hessian matrix.
Given a function f(x), its gradient (\nabla f), and positive definite Hessian matrix B, the Taylor series is:
:f(x_k+s_k)=f(x_k)+\nabla f(x_k)^T s_k+\frac s^T_k s_k,
and the Taylor series of the gradient itself (secant equation):
:\nabla f(x_k+s_k)=\nabla f(x_k)+B s_k,
is used to update B.
The DFP formula finds a solution that is symmetric, positive definite and closest to the current approximate value of B_k:
:B_=
(I-\gamma_k y_k s_k^T) B_k (I-\gamma_k s_k y_k^T)+\gamma_k y_k y_k^T,
where
:y_k=\nabla f(x_k+s_k)-\nabla f(x_k),
:\gamma_k =\frac.
and B_k is a symmetric and positive definite matrix.
The corresponding update to the inverse Hessian approximation H_k=B_k^ is given by:
:H_=H_-\frac+\frac.
B is assumed to be positive definite, and
the vectors s_k^T and y must satisfy the curvature condition:
: s_k^T y_k=s_k^T B s_k>0. \,
The DFP formula is quite effective, but it was soon superseded by the BFGS formula, which is its dual (interchanging the roles of y and s).
==See also==

* Newton's method
* Newton's method in optimization
* Quasi-Newton method
* Broyden–Fletcher–Goldfarb–Shanno (BFGS) method
* L-BFGS method
* SR1 formula
* Nelder–Mead method

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「Davidon–Fletcher–Powell formula」の詳細全文を読む



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