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 | ||
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
