32aad711457ec2d04aa5d0985cd4286769bb79cc
[octave.git] / optimization / steepest_descent.m
1 function [x, k] = steepest_descent(g, x0, step_size, tolerance, max_iterations)
2 %
3 % An implementation of the steepest-descent algorithm, with the
4 % search direction d_{k} = -\nabla f(x_{k}).
5 %
6 % We should terminate when either,
7 %
8 % a) The 2-norm of the gradient at x_{k} is greater than
9 % ``tolerance``.
10 %
11 % b) We have performed ``max_iterations`` iterations.
12 %
13 % INPUT:
14 %
15 % * ``g`` - the gradient of the function ``f`` we're minimizing.
16 %
17 % * ``x0`` - the starting point for the search.
18 %
19 % * ``step_size`` - a function of x which returns the optimal step
20 % size alpha_{k}. This will generally require more information
21 % than just x; for example it might need the function ``f`` or the
22 % gradient ``g``. However, our caller has this information so it
23 % should be incorporated into the step size algorithm that we
24 % receive.
25 %
26 % * ``tolerance`` - the stopping tolerance. We stop when the norm
27 % of the gradient falls below this value.
28 %
29 % * ``max_iterations`` - a safety net; we return x_{k}
30 % unconditionally if we exceed this number of iterations.
31 %
32 % OUTPUT:
33 %
34 % * ``x`` - the solution at the final value of x_{k}.
35 %
36 % * ``k`` - the value of k when we stop; i.e. the number of
37 % iterations.
38
39 % The initial gradient at x_{0} is not supplied, so we compute it
40 % here and begin the loop at k=1.
41 x = x0;
42 g_k = g(x);
43
44 if (norm(g_k) < tolerance)
45 % If x_0 is close enough to a solution, there's nothing for us to
46 % do! We use g_k (the gradient of f at x_k) instead of d_k because
47 % their 2-norms will be the same, and g_k is already stored.
48 return;
49 end
50
51 for k = [1 : max_iterations]
52 % Loop until either of our stopping conditions are met. If the
53 % loop finishes, we have implicitly met the second stopping
54 % condition (number of iterations).
55 d_k = -g_k;
56 alpha_k = step_size(x);
57 x = x + (alpha_k * d_k);
58 g_k = g(x);
59
60 if (norm(g_k) < tolerance)
61 return;
62 end
63 end
64
65 % If we make it to the end of the loop, that means we've executed the
66 % maximum allowed iterations. The caller should be able to examine the
67 % return value ``k`` to determine what happened.
68 end