[x, options] = scg(f, x, options, gradf) uses a scaled conjugate
gradients
algorithm to find a local minimum of the function f(x) whose
gradient is given by gradf(x). Here x is a row vector
and f returns a scalar value.
The point at which f has a local minimum
is returned as x. The function value at that point is returned
in options(8).
[x, options, flog, pointlog, scalelog] = scg(f, x, options, gradf)
also returns (optionally) a log of the function values
after each cycle in flog, a log
of the points visited in pointlog, and a log of the scale values
in the algorithm in scalelog.
scg(f, x, options, gradf, p1, p2, ...) allows
additional arguments to be passed to f() and gradf().
The optional parameters have the following interpretations.
options(1) is set to 1 to display error values; also logs error
values in the return argument errlog, and the points visited
in the return argument pointslog. If options(1) is set to 0,
then only warning messages are displayed. If options(1) is -1,
then nothing is displayed.
options(2) is a measure of the absolute precision required for the value
of x at the solution. If the absolute difference between
the values of x between two successive steps is less than
options(2), then this condition is satisfied.
options(3) is a measure of the precision required of the objective
function at the solution. If the absolute difference between the
objective function values between two successive steps is less than
options(3), then this condition is satisfied.
Both this and the previous condition must be
satisfied for termination.
options(9) is set to 1 to check the user defined gradient function.
options(10) returns the total number of function evaluations (including
those in any line searches).
options(11) returns the total number of gradient evaluations.
options(14) is the maximum number of iterations; default 100.
w = scg('neterr', w, options, 'netgrad', net, x, t);
nparams
successful weight updates where nparams is the total number of
parameters in x. The algorithm is based on that given by Williams
(1991), with a simplified procedure for updating lambda when
rho < 0.25.
conjgrad, quasinewCopyright (c) Ian T Nabney (1996-9)