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. Like the related Davidon–Fletcher–Powell method, BFGS determines the descent direction by preconditioning the gradient with curvature information. It does so by gradually improving an approximation to the Hessian matrix of the loss function, obtained only from gradient evaluations (or approximate gradient evaluations) via a generalized secant method.

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enIn numerical optimization, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization problems. Like the related Davidon–Fletcher–Powell method, BFGS determines the descent direction by preconditioning the gradient with curvature information. It does so by gradually improving an approximation to the Hessian matrix of the loss function, obtained only from gradient evaluations (or approximate gradient evaluations) via a generalized secant method.
Has abstract
enIn numerical optimization, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization problems. Like the related Davidon–Fletcher–Powell method, BFGS determines the descent direction by preconditioning the gradient with curvature information. It does so by gradually improving an approximation to the Hessian matrix of the loss function, obtained only from gradient evaluations (or approximate gradient evaluations) via a generalized secant method. Since the updates of the BFGS curvature matrix do not require matrix inversion, its computational complexity is only , compared to in Newton's method. 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. The algorithm is named after Charles George Broyden, Roger Fletcher, Donald Goldfarb and David Shanno.
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Broyden–Fletcher–Goldfarb–Shanno algorithm
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ALGLIB
Artelys Knitro
BHHH algorithm
Category:Optimization algorithms and methods
Charles George Broyden
Computational complexity of mathematical operations
Confidence interval
Credible interval
Davidon–Fletcher–Powell formula
David Shanno
Descent direction
Donald Goldfarb
GNU Octave
GNU Scientific Library
Gradient
Gradient descent
Hessian matrix
Iterative method
John Wiley & Sons
Julia (programming language)
L-BFGS
Levenberg–Marquardt algorithm
Line search
Loss function
Mathematica
Matrix inverse
Matrix inversion
Maximum likelihood estimation
Nelder–Mead method
Newton's method in optimization
Nonlinear optimization
Numerical analysis
Optimization (mathematics)
Optimization Toolbox
Pattern search (optimization)
Positive definiteness
Preconditioned gradient descent
Quasi-Newton methods
R (programming language)
Roger Fletcher (mathematician)
SciPy
Secant method
Sherman–Morrison formula
Strongly convex function
Symmetric rank-one
Trust region
Wolfe conditions
SameAs
2fepT
BFGS-Verfahren
BFGS法
BFGS算法
m.066pcg
Méthode de Broyden-Fletcher-Goldfarb-Shanno
Q2877013
Алгоритм Бройдена — Флетчера — Гольдфарба — Шанно
Алгоритм Бройдена — Флетчера — Гольдфарба — Шанно
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Category:Optimization algorithms and methods
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