Note: This page has been translated by MathWorks. Please click here

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

If you have a multicore processor or access to a processor network,
you can use Parallel Computing Toolbox™ functions with `MultiStart`

.
This example shows how to find multiple minima in parallel for a problem,
using a processor with two cores. The problem is the same as in Multiple Local Minima Via MultiStart.

Write a function file to compute the objective:

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) ... .*r.^2./(r+1); f = g.*h; end

Create the problem structure:

problem = createOptimProblem('fminunc',... 'objective',@(x)sawtoothxy(x(1),x(2)),... 'x0',[100,-50],'options',... optimoptions(@fminunc,'Algorithm','quasi-newton'));

Validate the problem structure by running it:

[x,fval] = fminunc(problem) x = 8.4420 -110.2602 fval = 435.2573

Create a

`MultiStart`

object, and set the object to use parallel processing and iterative display:ms = MultiStart('UseParallel',true,'Display','iter');

Set up parallel processing:

parpool Starting parpool using the 'local' profile ... connected to 4 workers. ans = Pool with properties: Connected: true NumWorkers: 4 Cluster: local AttachedFiles: {} IdleTimeout: 30 minute(s) (30 minutes remaining) SpmdEnabled: true

Run the problem on 50 start points:

[x,fval,eflag,output,manymins] = run(ms,problem,50); Running the local solvers in parallel. Run Local Local Local Local First-order Index exitflag f(x) # iter F-count optimality 17 2 3953 4 21 0.1626 16 0 1331 45 201 65.02 34 0 7271 54 201 520.9 33 2 8249 4 18 2.968 ... Many iterations omitted ... 47 2 2740 5 21 0.0422 35 0 8501 48 201 424.8 50 0 1225 40 201 21.89 MultiStart completed some of the runs from the start points. 17 out of 50 local solver runs converged with a positive local solver exit flag.

Notice that the run indexes look random. Parallel

`MultiStart`

runs its start points in an unpredictable order.Notice that

`MultiStart`

confirms parallel processing in the first line of output, which states: "Running the local solvers in parallel."When finished, shut down the parallel environment:

delete(gcp) Parallel pool using the 'local' profile is shutting down.

For an example of how to obtain better solutions to this problem, see Example: Searching for a Better Solution. You can use parallel processing along with the techniques described in that example.

The results of `MultiStart`

runs are stochastic.
The timing of runs is stochastic, too. Nevertheless, some clear trends
are apparent in the following table. The data for the table came from
one run at each number of start points, on a machine with two cores.

Start Points | Parallel Seconds | Serial Seconds |
---|---|---|

50 | 3.6 | 3.4 |

100 | 4.9 | 5.7 |

200 | 8.3 | 10 |

500 | 16 | 23 |

1000 | 31 | 46 |

Parallel computing can be slower than serial when you use only a few start points. As the number of start points increases, parallel computing becomes increasingly more efficient than serial.

There are many factors that affect speedup (or slowdown) with parallel processing. For more information, see Improving Performance with Parallel Computing (Optimization Toolbox).

Was this topic helpful?