How to compute the gradient for logistic regression in Matlab? I'm trying to minimize function f, firstly I was using fminsearch but it works long time, that's why now I use fminunc, but there is one problem: I need function gradient for acceleration. 
f = @(w) sum(log(1 + exp(-t .* (phis * w'))))/size(phis, 1) + coef * w*w';
options = optimset('Display', 'notify', 'MaxFunEvals', 2e+6, 'MaxIter', 2e+6);
w = fminunc(f, ones(1, size(phis, 2)), options);



*

*phis size is NxN+1

*t size is Nx1

*coef is const


Can you help me please construct gradient for function f, coz I always get this warning:
Warning: Gradient must be provided for trust-region algorithm;
  using line-search algorithm instead.

 A: The gradient should be (by chain rule)
%the gradient
%helper function
expt =  @(w)(exp(-t .* (phis * w')));
%precompute -t * phis
tphis = -diag(t) * phis;  %or bsxfun(@times,t,phis);
%the gradient
gradf = @(w)((sum(bsxfun(@times,expt(w) ./ (1 + expt(w)), tphis),1)'/size(phis,1)) + 2*coef * w');

probably would be faster not to compute expt(w) twice per evaluation, so you can rewrite this in terms of another anonymous function which takes exptw as input.
also I may have goofed up the dimensions on the sum--it seems like you are using w as a row vector, which is somewhat nonstandard.
edit: as @whuber noted, this kind of thing is easy to screw up. I didn't actually try the code I had previously. the above should be correct now. To test it, I estimated the gradient numerically and compared to the 'exact' value, as below:
%set up the problem
N = 9;
phis = rand(N,N+1);
t = rand(N,1);
coef = rand(1);

%the objective
f = @(w)((sum(log(1 + exp(-t .* (phis * w'))),1) / size(phis, 1)) + coef * w*w');

%helper function
expt =  @(w)(exp(-t .* (phis * w')));
%precompute -t * phis
tphis = -diag(t) * phis;  %or bsxfun(@times,t,phis);
%the gradient
gradf = @(w)((sum(bsxfun(@times,expt(w) ./ (1 + expt(w)), tphis),1)'/size(phis,1)) + 2*coef * w');

%test the code now:
%compute the approximate gradient numerically
w0 = randn(1,N+1);
fw = f(w0);

%%the numerical:
delta = 1e-6;
eyeN = eye(N+1);

gfw = nan(size(w0));
for iii=1:numel(w0)
    gfw(iii) = (f(w0 + delta * eyeN(iii,:)) - fw) ./ delta;
end

%the 'exact':
truegfw = gradf(w0);

%report
fprintf('max difference between exact and numerical is %g\n',max(abs(truegfw' - gfw)));

when I run this (sorry, should have set the rand seed), I get:
max difference between exact and numerical is 4.80006e-07
YMMV, depending on the rand seed and the value of delta used.
