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Find Global or Multiple Local Minima

This example illustrates how GlobalSearch finds a global minimum efficiently, and how MultiStart finds many more local minima.

The objective function for this example has many local minima and a unique global minimum. In polar coordinates, the function is




Plot the functions g and h, and create a surface plot of the function f.

fplot(@(r)(sin(r) - sin(2*r)/2 + sin(3*r)/3 - sin(4*r)/4 + 4) .* r.^2./(r+1), [0 20])
title(''); ylabel('g'); xlabel('r');
fplot(@(t)2 + cos(t) + cos(2*t-1/2)/2, [0 2*pi])
title(''); ylabel('h'); xlabel('t');

Figure contains 2 axes objects. Axes object 1 with xlabel r, ylabel g contains an object of type functionline. Axes object 2 with xlabel t, ylabel h contains an object of type functionline.

fsurf(@(x,y) sawtoothxy(x,y), [-20 20])
% sawtoothxy is defined in the first step below
xlabel('x'); ylabel('y'); title('sawtoothxy(x,y)');

Figure contains an axes object. The axes object with title sawtoothxy(x,y), xlabel x, ylabel y contains an object of type functionsurface.

The global minimum is at r = 0, with objective function 0. The function g(r) grows approximately linearly in r, with a repeating sawtooth shape. The function h(t) has two local minima, one of which is global.

The sawtoothxy.m file converts from Cartesian to polar coordinates, then computes the value in polar coordinates.

type sawtoothxy
function f = sawtoothxy(x,y)
[t r] = cart2pol(x,y); % change to polar coordinates
h = cos(2*t - 1/2)/2 + cos(t) + 2;
g = (sin(r) - sin(2*r)/2 + sin(3*r)/3 - sin(4*r)/4 + 4) ...
f = g.*h;

Single Global Minimum Via GlobalSearch

To search for the global minimum using GlobalSearch, first create a problem structure. Use the 'sqp' algorithm for fmincon,

problem = createOptimProblem('fmincon',...

The start point is [100,-50] instead of [0,0] so GlobalSearch does not start at the global solution.

Validate the problem structure by running fmincon.

[x,fval] = fmincon(problem)
x = 1×2

   45.7236 -107.6515

fval = 555.5820

Create the GlobalSearch object, and set iterative display.

gs = GlobalSearch('Display','iter');

For reproducibility, set the random number generator seed.


Run the solver.

[x,fval] = run(gs,problem)
 Num Pts                 Best       Current    Threshold        Local        Local                 
Analyzed  F-count        f(x)       Penalty      Penalty         f(x)     exitflag        Procedure
       0      200       555.6                                   555.6            0    Initial Point
     200     1463   1.547e-15                               1.547e-15            1    Stage 1 Local
     300     1564   1.547e-15     5.858e+04        1.074                              Stage 2 Search
     400     1664   1.547e-15      1.84e+05         4.16                              Stage 2 Search
     500     1764   1.547e-15     2.683e+04        11.84                              Stage 2 Search
     600     1864   1.547e-15     1.122e+04        30.95                              Stage 2 Search
     700     1964   1.547e-15     1.353e+04        65.25                              Stage 2 Search
     800     2064   1.547e-15     6.249e+04        163.8                              Stage 2 Search
     900     2164   1.547e-15     4.119e+04        409.2                              Stage 2 Search
     950     2359   1.547e-15           477        589.7          387            2    Stage 2 Local
     952     2423   1.547e-15         368.4          477        250.7            2    Stage 2 Local
    1000     2471   1.547e-15     4.031e+04        530.9                              Stage 2 Search

GlobalSearch stopped because it analyzed all the trial points.

3 out of 4 local solver runs converged with a positive local solver exit flag.
x = 1×2
10-7 ×

    0.0414    0.1298

fval = 1.5467e-15

The solver finds three local minima, including the global minimum near [0,0].

Multiple Local Minima Via MultiStart

To search for multiple minima using MultiStart, first create a problem structure. Because the problem is unconstrained, use the fminunc solver. Set options not to show any display at the command line.

problem = createOptimProblem('fminunc',...

Validate the problem structure by running it.

[x,fval] = fminunc(problem)
x = 1×2

    8.4420 -110.2602

fval = 435.2573

Create a default MultiStart object.

ms = MultiStart;

Run the solver for 50 iterations, recording the local minima.

rng(1) % For reproducibility
[x,fval,eflag,output,manymins] = run(ms,problem,50)
MultiStart completed some of the runs from the start points. 

10 out of 50 local solver runs converged with a positive local solver exitflag.
x = 1×2

  -73.8348 -197.7810

fval = 766.8260
eflag = 2
output = struct with fields:
                funcCount: 8583
         localSolverTotal: 50
       localSolverSuccess: 10
    localSolverIncomplete: 40
    localSolverNoSolution: 0
                  message: 'MultiStart completed some of the runs from the start points. ...'

manymins=1×10 object
  1x10 GlobalOptimSolution array with properties:


The solver does not find the global minimum near [0,0]. The solver finds 10 distinct local minima.

Plot the function values at the local minima:


Figure contains an axes object. The axes object contains an object of type histogram.

Plot the function values at the three best points:

bestf = [manymins.Fval];

Figure contains an axes object. The axes object contains an object of type histogram.

MultiStart starts fminunc from start points with components uniformly distributed between –1000 and 1000. fminunc often gets stuck in one of the many local minima. fminunc exceeds its iteration limit or function evaluation limit 40 times.

See Also

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