# Difference between revisions of "Lbfgs.m"

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==Arguments== | ==Arguments== | ||

− | dx_hist - history of x increments, | + | dx_hist - history of x increments, a stack |

− | + | of column vectors, from the latest | |

+ | to the earliest | ||

dg_hist - history of gradient increments, | dg_hist - history of gradient increments, | ||

− | + | a stack of column vectors, from | |

− | + | the latest to the earliest | |

+ | |||

g - current gradient | g - current gradient | ||

## Latest revision as of 17:27, 13 August 2018

Calculates an approximation to the Newton-Raphson search direction using past gradients to build a serviceable substitute to a Hessian. The Hessian matrix is never explicitly formed or inverted. This function is the implementation from section 4 of http://dx.doi.org/10.1090/S0025-5718-1980-0572855-7

## Contents

## Syntax

direction=lbfgs(dx_hist,dg_hist,g)

## Arguments

dx_hist - history of x increments, a stack of column vectors, from the latest to the earliest dg_hist - history of gradient increments, a stack of column vectors, from the latest to the earliest g - current gradient

## Output

direction - LBFGS approximation to the search direction

## Notes

The L-BFGS algorithm is the default of fminnewton.m, and is a good mix of computational efficiency and fast convergence.

## See also

fminnewton.m, hessreg.m, linesearch.m

*Version 2.2, authors: Ilya Kuprov, David Goodwin*