## Isolated Global Minimum

### Difficult-To-Locate Global Minimum

Finding a start point in the basin of attraction of the global minimum can be difficult when the basin is small or when you are unsure of the location of the minimum. To solve this type of problem you can:

Add sensible bounds

Take a huge number of random start points

Make a methodical grid of start points

For an unconstrained problem, take widely dispersed random start points

This example shows these methods and some variants.

The function –sech(*x*) is nearly 0 for all |*x*| > 5, and –sech(0) = –1. The example is a two-dimensional version of the sech function,
with one minimum at `[1,1]`

, the other at
`[1e5,-1e5]`

:

*f*(*x*,*y*) =
–10sech(|*x* – (1,1)|) – 20sech(.0003(|x – (1e5,–1e5)|) –
1.

*f* has a global minimum of –21 at (1e5,–1e5), and a local
minimum of –11 at (1,1).

Code for Generating the Figure

The minimum at (1e5,–1e5) shows as a narrow spike. The minimum at (1,1) does not show since it is too narrow.

The following sections show various methods of searching for the global minimum. Some of the methods are not successful on this problem. Nevertheless, you might find each method useful for different problems.

### Default Settings Cannot Find the Global Minimum — Add Bounds

`GlobalSearch`

and `MultiStart`

cannot find the global minimum using default global
options, since the default start point components are in the range (–9999,10001) for
`GlobalSearch`

and (–1000,1000) for `MultiStart`

.

With additional bounds of –1e6 and 1e6 in `problem`

, `GlobalSearch`

usually does not find the global
minimum:

x1 = [1;1];x2 = [1e5;-1e5]; f = @(x)-10*sech(norm(x(:)-x1)) -20*sech((norm(x(:)-x2))*3e-4) -1; opts = optimoptions(@fmincon,'Algorithm','active-set'); problem = createOptimProblem('fmincon','x0',[0,0],'objective',f,... 'lb',[-1e6;-1e6],'ub',[1e6;1e6],'options',opts); gs = GlobalSearch; rng(14,'twister') % for reproducibility [xfinal,fval] = run(gs,problem)

GlobalSearch stopped because it analyzed all the trial points. All 32 local solver runs converged with a positive local solver exit flag. xfinal = 1.0000 1.0000 fval = -11.0000

### GlobalSearch with Bounds and More Start Points

To find the global minimum, you can search more points. This example uses 1e5
start points, and a `MaxTime`

of 300 s:

gs.NumTrialPoints = 1e5; gs.MaxTime = 300; [xg,fvalg] = run(gs,problem)

GlobalSearch stopped because maximum time is exceeded. GlobalSearch called the local solver 2186 times before exceeding the clock time limit (MaxTime = 300 seconds). 1943 local solver runs converged with a positive local solver exit flag. xg = 1.0e+04 * 10.0000 -10.0000 fvalg = -21.0000

In this case, `GlobalSearch`

found the global
minimum.

### MultiStart with Bounds and Many Start Points

Alternatively, you can search using `MultiStart`

with many start points. This example uses 1e5 start points, and a
`MaxTime`

of 300 s:

ms = MultiStart(gs); [xm,fvalm] = run(ms,problem,1e5)

MultiStart stopped because maximum time was exceeded. MultiStart called the local solver 17266 times before exceeding the clock time limit (MaxTime = 300 seconds). 17266 local solver runs converged with a positive local solver exit flag. xm = 1.0000 1.0000 fvalm = -11.0000

In this case, `MultiStart`

failed to find the
global minimum.

### MultiStart Without Bounds, Widely Dispersed Start Points

You can also use `MultiStart`

to search an
unbounded region to find the global minimum. Again, you need many start points to
have a good chance of finding the global minimum.

The first five lines of code generate 10,000 widely dispersed random start points
using the method described in Widely Dispersed Points for Unconstrained Components.
`newprob`

is a problem structure using the
`fminunc`

local solver and no bounds:

rng(0,'twister') % for reproducibility u = rand(1e4,1); u = 1./u; u = exp(u) - exp(1); s = rand(1e4,1)*2*pi; stpts = [u.*cos(s),u.*sin(s)]; startpts = CustomStartPointSet(stpts); opts = optimoptions(@fminunc,'Algorithm','quasi-newton'); newprob = createOptimProblem('fminunc','x0',[0;0],'objective',f,... 'options',opts); [xcust,fcust] = run(ms,newprob,startpts)

MultiStart completed the runs from all start points. All 10000 local solver runs converged with a positive local solver exit flag. xcust = 1.0e+05 * 1.0000 -1.0000 fcust = -21.0000

In this case, `MultiStart`

found the global
minimum.

### MultiStart with a Regular Grid of Start Points

You can also use a grid of start points instead of random start points. To learn how to construct a regular grid for more dimensions, or one that has small perturbations, see Uniform Grid or Perturbed Grid.

xx = -1e6:1e4:1e6; [xxx,yyy] = meshgrid(xx,xx); z = [xxx(:),yyy(:)]; bigstart = CustomStartPointSet(z); [xgrid,fgrid] = run(ms,newprob,bigstart)

MultiStart completed the runs from all start points. All 10000 local solver runs converged with a positive local solver exit flag. xgrid = 1.0e+004 * 10.0000 -10.0000 fgrid = -21.0000

In this case, `MultiStart`

found the global
minimum.

### MultiStart with Regular Grid and Promising Start Points

Making a regular grid of start points, especially in high dimensions, can use an inordinate amount of memory or time. You can filter the start points to run only those with small objective function value.

To perform this filtering most efficiently, write your objective function in a vectorized fashion. For information, see Write a Vectorized Function or Vectorize the Objective and Constraint Functions. The following function handle computes a vector of objectives based on an input matrix whose rows represent start points:

```
x1 = [1;1];x2 = [1e5;-1e5];
g = @(x) -10*sech(sqrt((x(:,1)-x1(1)).^2 + (x(:,2)-x1(2)).^2)) ...
-20*sech(sqrt((x(:,1)-x2(1)).^2 + (x(:,2)-x2(2)).^2))-1;
```

Suppose you want to run the local solver only for points where the value is less than –2. Start with a denser grid than in MultiStart with a Regular Grid of Start Points, then filter out all the points with high function value:

xx = -1e6:1e3:1e6; [xxx,yyy] = meshgrid(xx,xx); z = [xxx(:),yyy(:)]; idx = g(z) < -2; % index of promising start points zz = z(idx,:); smallstartset = CustomStartPointSet(zz); opts = optimoptions(@fminunc,'Algorithm','quasi-newton','Display','off'); newprobg = createOptimProblem('fminunc','x0',[0,0],... 'objective',g,'options',opts); % row vector x0 since g expects rows [xfew,ffew] = run(ms,newprobg,smallstartset)

MultiStart completed the runs from all start points. All 2 local solver runs converged with a positive local solver exit flag. xfew = 100000 -100000 ffew = -21

In this case, `MultiStart`

found the global
minimum. There are only two start points in `smallstartset`

, one of
which is the global minimum.