## Optimizing Nonlinear Functions

### Minimizing Functions of One Variable

Given a mathematical function of a single variable, you can use the `fminbnd`

function to find a local minimizer of the function in a given interval. For example, consider the `humps.m`

function, which is provided with MATLAB®. The following figure shows the graph of `humps`

.

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 `(0.3,1)`

, use

x = fminbnd(@humps,0.3,1)

x = 0.6370

You can see details of the solution process by using `optimset`

to create options with the `Display`

option set to `'iter'`

. Pass the resulting options to `fminbnd`

.

options = optimset('Display','iter'); x = fminbnd(@humps,0.3,1,options)

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

The iterative display shows the current value of `x`

and the function value at `f(x)`

each time a function evaluation occurs. For `fminbnd`

, one function evaluation corresponds to one iteration of the algorithm. The last column shows the procedure `fminbnd`

uses at each iteration, a golden section search or a parabolic interpolation. For details, see Optimization Solver Iterative Display.

**Note: **Optimization solvers apply to real-valued functions. Complex values cannot be optimized, except for a real-valued function of the complex values, such as the norm.

### Minimizing Functions of Several Variables

The `fminsearch`

function
is similar to `fminbnd`

except that it handles functions
of many variables. Specify a starting vector *x*_{0} 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.

To try `fminsearch`

, create a function `three_var`

of
three variables, `x`

, `y`

, and `z`

.

```
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`

, ```
y
= -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

**Note**

Optimization solvers apply to real-valued functions. Complex values cannot be optimized, except for a real-valued function of the complex values, such as the norm.

### Maximizing Functions

The `fminbnd`

and `fminsearch`

solvers
attempt to minimize an objective function. If you have a maximization
problem, that is, a problem of the form

$$\underset{x}{\mathrm{max}}f(x),$$

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 `fval`

), 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* = 2*π* = 6.2832.

`fminsearch`

Algorithm

`fminsearch`

uses the Nelder-Mead simplex
algorithm as described in Lagarias et al. [1]. This algorithm uses a simplex of *n* + 1 points for *n*-dimensional
vectors *x*. The algorithm first makes a simplex
around the initial guess *x*_{0} by
adding 5% of each component *x*_{0}(*i*)
to *x*_{0}. The algorithm uses
these *n* vectors as elements of the simplex in addition
to *x*_{0}. (The algorithm uses
0.00025 as component *i* if *x*_{0}(*i*) = 0.) Then, the
algorithm modifies the simplex repeatedly according to the following
procedure.

**Note**

The keywords for the `fminsearch`

iterative
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*= 2*m*–*x*(*n*+ 1),**(1)**where

*m*= Σ*x*(*i*)/*n*,*i*= 1...*n*,**(2)**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)),**(3)**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 either*x*(*n*+ 1) or*r*, depending on which has the lower objective function value.If

*f*(*r*) <*f*(*x*(*n*+ 1)) (that is,*r*is better than*x*(*n*+ 1)), calculate*c*=*m*+ (*r*–*m*)/2**(4)**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**(5)**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**(6)**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 `fminsearch`

can
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.

### Reference

[1] 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.

## Related Topics

- Optimization Troubleshooting and Tips
- Nonlinear Optimization (Optimization Toolbox)
- Curve Fitting via Optimization