Given a mathematical function of a single variable, you can
fminbnd function to
find a local minimizer of the function in a given interval. For example,
which is provided with MATLAB®. The following figure shows the
x = -1:.01:2; y = humps(x); plot(x,y) xlabel('x') ylabel('humps(x)') grid on
To find the minimum of the
humps function in the range
x = fminbnd(@humps,0.3,1)
x = 0.6370
You can ask for a tabular display of output by passing a fourth argument created by the
optimset command to
opts = optimset('Display','iter'); x = fminbnd(@humps,0.3,1,opts)
Func-count x f(x) Procedure 1 0.567376 12.9098 initial 2 0.732624 13.7746 golden 3 0.465248 25.1714 golden 4 0.644416 11.2693 parabolic 5 0.6413 11.2583 parabolic 6 0.637618 11.2529 parabolic 7 0.636985 11.2528 parabolic 8 0.637019 11.2528 parabolic 9 0.637052 11.2528 parabolic Optimization terminated: the current x satisfies the termination criteria using OPTIONS.TolX of 1.000000e-04 x = 0.6370
This shows the current value of
x and the
function value at
f(x) each time a function evaluation
fminbnd, one function evaluation corresponds
to one iteration of the algorithm. The last column shows what procedure
is being used at each iteration, either a golden section search or
a parabolic interpolation. For more information, see Iterative Display.
is similar to
fminbnd except that it handles functions
of many variables, and you specify a starting vector x0 rather
than a starting interval.
fminsearch attempts to
return a vector x that is a local minimizer of
the mathematical function near this starting vector.
fminsearch, create a function
function b = three_var(v) x = v(1); y = v(2); z = v(3); b = x.^2 + 2.5*sin(y) - z^2*x^2*y^2;
Now find a minimum for this function using
x = -0.6,
= -1.2, and
z = 0.135 as the starting values.
v = [-0.6,-1.2,0.135]; a = fminsearch(@three_var,v) a = 0.0000 -1.5708 0.1803
attempt to minimize an objective function. If you have a maximization
problem, that is, a problem of the form
then define g(x) = –f(x), and minimize g.
For example, to find the maximum of tan(cos(x)) near x = 5, evaluate:
[x fval] = fminbnd(@(x)-tan(cos(x)),3,8) x = 6.2832 fval = -1.5574
The maximum is 1.5574 (the negative of the reported
and occurs at x = 6.2832. This answer is correct since,
to five digits, the maximum is tan(1) = 1.5574, which occurs at x = 2π = 6.2832.
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
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.
 Lagarias, J. C., J. A. Reeds, M. H. Wright, and P. E. Wright. "Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions." SIAM Journal of Optimization, Vol. 9, Number 1, 1998, pp. 112–147.