fminsearch uses the Nelder-Mead simplex
algorithm as described in Lagarias et al. . This algorithm uses a simplex of n + 1 points for n-dimensional
vectors x. The algorithm first makes a simplex
around the initial guess x0 by
adding 5% of each component x0(i)
to x0, and using these n vectors
as elements of the simplex in addition to x0.
(It uses 0.00025 as component i if x0(i) = 0.) Then, the
algorithm modifies the simplex repeatedly according to the following
The keywords for the
display appear in bold after the
description of the step.
Let x(i) denote the list of points in the current simplex, i = 1,...,n+1.
Order the points in the simplex from lowest function value f(x(1)) to highest f(x(n+1)). At each step in the iteration, the algorithm discards the current worst point x(n+1), and accepts another point into the simplex. [Or, in the case of step 7 below, it changes all n points with values above f(x(1))].
Generate the reflected point
r = 2m – x(n+1),
m = Σx(i)/n, i = 1...n,
and calculate f(r).
If f(x(1)) ≤ f(r) < f(x(n)), accept r and terminate this iteration. Reflect
If f(r) < f(x(1)), calculate the expansion point s
s = m + 2(m – x(n+1)),
and calculate f(s).
If f(s) < f(r), accept s and terminate the iteration. Expand
Otherwise, accept r and terminate the iteration. Reflect
If f(r) ≥ f(x(n)), perform a contraction between m and the better of x(n+1) and r:
If f(r) < f(x(n+1)) (i.e., r is better than x(n+1)), calculate
c = m + (r – m)/2
and calculate f(c). If f(c) < f(r), accept c and terminate the iteration. Contract outside Otherwise, continue with Step 7 (Shrink).
If f(r) ≥ f(x(n+1)), calculate
cc = m + (x(n+1) – m)/2
and calculate f(cc). If f(cc) < f(x(n+1)), accept cc and terminate the iteration. Contract inside Otherwise, continue with Step 7 (Shrink).
Calculate the n points
v(i) = x(1) + (x(i) – x(1))/2
and calculate f(v(i)), i = 2,...,n+1. The simplex at the next iteration is x(1), v(2),...,v(n+1). Shrink
The following figure shows the points that
calculate in the procedure, along with each possible new simplex.
The original simplex has a bold outline. The iterations proceed until
they meet a stopping criterion.