Minimizing an Expensive Optimization Problem Using Parallel Computing Toolbox
This example shows how to speed up the minimization of an expensive optimization problem using functions in Optimization Toolbox™ and Global Optimization Toolbox. In the first part of the example we solve the optimization problem by evaluating functions in a serial fashion, and in the second part of the example we solve the same problem using the parallel for loop (parfor
) feature by evaluating functions in parallel. We compare the time taken by the optimization function in both cases.
Expensive Optimization Problem
For the purpose of this example, we solve a problem in four variables, where the objective and constraint functions are made artificially expensive by pausing.
function f = expensive_objfun(x) %EXPENSIVE_OBJFUN An expensive objective function used in optimparfor example. % Copyright 2007-2013 The bat365, Inc. % Simulate an expensive function by pausing pause(0.1) % Evaluate objective function f = exp(x(1)) * (4*x(3)^2 + 2*x(4)^2 + 4*x(1)*x(2) + 2*x(2) + 1);
function [c,ceq] = expensive_confun(x) %EXPENSIVE_CONFUN An expensive constraint function used in optimparfor example. % Copyright 2007-2013 The bat365, Inc. % Simulate an expensive function by pausing pause(0.1); % Evaluate constraints c = [1.5 + x(1)*x(2)*x(3) - x(1) - x(2) - x(4); -x(1)*x(2) + x(4) - 10]; % No nonlinear equality constraints: ceq = [];
Minimizing Using fmincon
We are interested in measuring the time taken by fmincon
in serial so that we can compare it to the parallel time.
startPoint = [-1 1 1 -1]; options = optimoptions('fmincon','Display','iter','Algorithm','interior-point'); startTime = tic; xsol = fmincon(@expensive_objfun,startPoint,[],[],[],[],[],[],@expensive_confun,options); time_fmincon_sequential = toc(startTime); fprintf('Serial FMINCON optimization takes %g seconds.\n',time_fmincon_sequential);
First-order Norm of Iter F-count f(x) Feasibility optimality step 0 5 1.839397e+00 1.500e+00 3.211e+00 1 11 -9.760099e-01 3.708e+00 7.902e-01 2.362e+00 2 16 -1.480976e+00 0.000e+00 8.344e-01 1.069e+00 3 21 -2.601599e+00 0.000e+00 8.390e-01 1.218e+00 4 29 -2.823630e+00 0.000e+00 2.598e+00 1.118e+00 5 34 -3.905338e+00 0.000e+00 1.210e+00 7.302e-01 6 39 -6.212992e+00 3.934e-01 7.372e-01 2.405e+00 7 44 -5.948761e+00 0.000e+00 1.784e+00 1.905e+00 8 49 -6.940062e+00 1.233e-02 7.668e-01 7.553e-01 9 54 -6.973887e+00 0.000e+00 2.549e-01 3.920e-01 10 59 -7.142993e+00 0.000e+00 1.903e-01 4.735e-01 11 64 -7.155325e+00 0.000e+00 1.365e-01 2.626e-01 12 69 -7.179122e+00 0.000e+00 6.336e-02 9.115e-02 13 74 -7.180116e+00 0.000e+00 1.069e-03 4.670e-02 14 79 -7.180409e+00 0.000e+00 7.797e-04 2.815e-03 15 84 -7.180410e+00 0.000e+00 6.368e-06 3.120e-04 Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance. Serial FMINCON optimization takes 18.2876 seconds.
Minimizing Using Genetic Algorithm
Since ga
usually takes many more function evaluations than fmincon
, we remove the expensive constraint from this problem and perform unconstrained optimization instead. Pass empty matrices []
for constraints. In addition, we limit the maximum number of generations to 15 for ga
so that ga
can terminate in a reasonable amount of time. We are interested in measuring the time taken by ga
so that we can compare it to the parallel ga
evaluation. Note that running ga
requires Global Optimization Toolbox.
rng default % for reproducibility try gaAvailable = false; nvar = 4; gaoptions = optimoptions('ga','MaxGenerations',15,'Display','iter'); startTime = tic; gasol = ga(@expensive_objfun,nvar,[],[],[],[],[],[],[],gaoptions); time_ga_sequential = toc(startTime); fprintf('Serial GA optimization takes %g seconds.\n',time_ga_sequential); gaAvailable = true; catch ME warning(message('optim:optimdemos:optimparfor:gaNotFound')); end
Single objective optimization: 4 Variable(s) Options: CreationFcn: @gacreationuniform CrossoverFcn: @crossoverscattered SelectionFcn: @selectionstochunif MutationFcn: @mutationgaussian Best Mean Stall Generation Func-count f(x) f(x) Generations 1 100 -5.546e+05 1.483e+15 0 2 147 -5.581e+17 -1.116e+16 0 3 194 -7.556e+17 6.679e+22 0 4 241 -7.556e+17 -7.195e+16 1 5 288 -9.381e+27 -1.876e+26 0 6 335 -9.673e+27 -7.497e+26 0 7 382 -4.511e+36 -9.403e+34 0 8 429 -5.111e+36 -3.011e+35 0 9 476 -7.671e+36 9.346e+37 0 10 523 -1.52e+43 -3.113e+41 0 11 570 -2.273e+45 -4.67e+43 0 12 617 -2.589e+47 -6.281e+45 0 13 664 -2.589e+47 -1.015e+46 1 14 711 -8.149e+47 -5.855e+46 0 15 758 -9.503e+47 -1.29e+47 0 Optimization terminated: maximum number of generations exceeded. Serial GA optimization takes 81.5878 seconds.
Setting Parallel Computing Toolbox
The finite differencing used by the functions in Optimization Toolbox to approximate derivatives is done in parallel using the parfor
feature if Parallel Computing Toolbox™ is available and there is a parallel pool of workers. Similarly, ga
, gamultiobj
, and patternsearch
solvers in Global Optimization Toolbox evaluate functions in parallel. To use the parfor
feature, we use the parpool
function to set up the parallel environment. The computer on which this example is published has four cores, so parpool
starts four MATLAB® workers. If there is already a parallel pool when you run this example, we use that pool; see the documentation for parpool
for more information.
if max(size(gcp)) == 0 % parallel pool needed parpool % create the parallel pool end
Minimizing Using Parallel fmincon
To minimize our expensive optimization problem using the parallel fmincon
function, we need to explicitly indicate that our objective and constraint functions can be evaluated in parallel and that we want fmincon
to use its parallel functionality wherever possible. Currently, finite differencing can be done in parallel. We are interested in measuring the time taken by fmincon
so that we can compare it to the serial fmincon
run.
options = optimoptions(options,'UseParallel',true); startTime = tic; xsol = fmincon(@expensive_objfun,startPoint,[],[],[],[],[],[],@expensive_confun,options); time_fmincon_parallel = toc(startTime); fprintf('Parallel FMINCON optimization takes %g seconds.\n',time_fmincon_parallel);
First-order Norm of Iter F-count f(x) Feasibility optimality step 0 5 1.839397e+00 1.500e+00 3.211e+00 1 11 -9.760099e-01 3.708e+00 7.902e-01 2.362e+00 2 16 -1.480976e+00 0.000e+00 8.344e-01 1.069e+00 3 21 -2.601599e+00 0.000e+00 8.390e-01 1.218e+00 4 29 -2.823630e+00 0.000e+00 2.598e+00 1.118e+00 5 34 -3.905338e+00 0.000e+00 1.210e+00 7.302e-01 6 39 -6.212992e+00 3.934e-01 7.372e-01 2.405e+00 7 44 -5.948761e+00 0.000e+00 1.784e+00 1.905e+00 8 49 -6.940062e+00 1.233e-02 7.668e-01 7.553e-01 9 54 -6.973887e+00 0.000e+00 2.549e-01 3.920e-01 10 59 -7.142993e+00 0.000e+00 1.903e-01 4.735e-01 11 64 -7.155325e+00 0.000e+00 1.365e-01 2.626e-01 12 69 -7.179122e+00 0.000e+00 6.336e-02 9.115e-02 13 74 -7.180116e+00 0.000e+00 1.069e-03 4.670e-02 14 79 -7.180409e+00 0.000e+00 7.797e-04 2.815e-03 15 84 -7.180410e+00 0.000e+00 6.368e-06 3.120e-04 Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance. Parallel FMINCON optimization takes 8.79291 seconds.
Minimizing Using Parallel Genetic Algorithm
To minimize our expensive optimization problem using the ga
function, we need to explicitly indicate that our objective function can be evaluated in parallel and that we want ga
to use its parallel functionality wherever possible. To use the parallel ga
we also require that the 'Vectorized' option be set to the default (i.e., 'off'). We are again interested in measuring the time taken by ga
so that we can compare it to the serial ga
run. Though this run may be different from the serial one because ga
uses a random number generator, the number of expensive function evaluations is the same in both runs. Note that running ga
requires Global Optimization Toolbox.
rng default % to get the same evaluations as the previous run if gaAvailable gaoptions = optimoptions(gaoptions,'UseParallel',true); startTime = tic; gasol = ga(@expensive_objfun,nvar,[],[],[],[],[],[],[],gaoptions); time_ga_parallel = toc(startTime); fprintf('Parallel GA optimization takes %g seconds.\n',time_ga_parallel); end
Single objective optimization: 4 Variable(s) Options: CreationFcn: @gacreationuniform CrossoverFcn: @crossoverscattered SelectionFcn: @selectionstochunif MutationFcn: @mutationgaussian Best Mean Stall Generation Func-count f(x) f(x) Generations 1 100 -5.546e+05 1.483e+15 0 2 147 -5.581e+17 -1.116e+16 0 3 194 -7.556e+17 6.679e+22 0 4 241 -7.556e+17 -7.195e+16 1 5 288 -9.381e+27 -1.876e+26 0 6 335 -9.673e+27 -7.497e+26 0 7 382 -4.511e+36 -9.403e+34 0 8 429 -5.111e+36 -3.011e+35 0 9 476 -7.671e+36 9.346e+37 0 10 523 -1.52e+43 -3.113e+41 0 11 570 -2.273e+45 -4.67e+43 0 12 617 -2.589e+47 -6.281e+45 0 13 664 -2.589e+47 -1.015e+46 1 14 711 -8.149e+47 -5.855e+46 0 15 758 -9.503e+47 -1.29e+47 0 Optimization terminated: maximum number of generations exceeded. Parallel GA optimization takes 15.2253 seconds.
Compare Serial and Parallel Time
X = [time_fmincon_sequential time_fmincon_parallel]; Y = [time_ga_sequential time_ga_parallel]; t = [0 1]; plot(t,X,'r--',t,Y,'k-') ylabel('Time in seconds') legend('fmincon','ga') ax = gca; ax.XTick = [0 1]; ax.XTickLabel = {'Serial' 'Parallel'}; axis([0 1 0 ceil(max([X Y]))]) title('Serial Vs. Parallel Times')
Utilizing parallel function evaluation via parfor
improved the efficiency of both fmincon
and ga
. The improvement is typically better for expensive objective and constraint functions.