## Loren on the Art of MATLABTurn ideas into MATLAB

Note

Loren on the Art of MATLAB has been retired and will not be updated.

# Finding the Optimal Value

Have you ever needed to solve an optimization problem where there were local minima? What strategy do you use to solve it, trying to find the "best" answer? Today I'm going to talk about a simple strategy, readily available in the Global Optimization Toolbox.

## Solve a Simple Problem

Or at least let's try. I have some data and I want to fit a particular form of a curve to it. First let's look at the pharmacokinetic data. Here's the reference: Parameter estimation in nonlinear algebraic models via global optimization. Computers & Chemical Engineering, Volume 22, Supplement 1, 15 March 1998, Pages S213-S220 William R. Esposito, Christodoulos A. Floudas.
The data are time vs. concentration
t = [ 3.92, 7.93, 11.89, 23.90, 47.87, 71.91, 93.85, 117.84 ]
t = 1×8
3.9200 7.9300 11.8900 23.9000 47.8700 71.9100 93.8500 117.8400
c = [0.163, 0.679, 0.679, 0.388, 0.183, 0.125, 0.086, 0.0624 ]
c = 1×8
0.1630 0.6790 0.6790 0.3880 0.1830 0.1250 0.0860 0.0624
I like to see the data, in part to be sure I have no entry mistakes, and in part to get a feel for the overall system. In fact, let's visualize the data.
plot(t,c,'o')
xlabel('Time')
ylabel('Concentration')

## 3 Compartment Model

As in the reference, we fit a 3 compartment model, sum of 3 decaying exponentials.
$\mathit{c}={\mathit{b}}_{1}\text{\hspace{0.17em}}{\mathit{e}}^{\left(-{\mathit{b}}_{4}\text{\hspace{0.17em}}\mathit{t}\right)}+{\mathit{b}}_{2}\text{\hspace{0.17em}}{\mathit{e}}^{\left({-\mathit{b}}_{5}\text{\hspace{0.17em}}\mathit{t}\right)}+{\mathit{b}}_{3}\text{\hspace{0.17em}}{\mathit{e}}^{\left({-\mathit{b}}_{6}\text{\hspace{0.17em}}\mathit{t}\right)}$
and we can express that model as an anonymous function of t (time) and the model parameters [b(1) b(2) ... b(6)].
model = @(b,t) b(1)*exp(-b(4)*t) + b(2)*exp(-b(5)*t) + b(3)*exp(-b(6)*t)
model = function_handle with value: @(b,t)b(1)*exp(-b(4)*t)+b(2)*exp(-b(5)*t)+b(3)*exp(-b(6)*t)

## Define Optimization Problem

We next define the optimization problem to solve using the problem-based formulation. This allows us to choose the solver we want, supply the data, and naturally express constraints and options.
problem = createOptimProblem('lsqcurvefit', ...
'objective', model, ...
'xdata', t, 'ydata', c, ...
'x0',ones(1,6),...
'lb', [-10 -10 -10 0 0 0 ],...
'ub', [ 10 10 10 0.5 0.5 0.5], ...
'options',optimoptions('lsqcurvefit',...
'OutputFcn', @curvefittingPlotIterates,...
'Display','none'))
problem = struct with fields:
objective: @(b,t)b(1)*exp(-b(4)*t)+b(2)*exp(-b(5)*t)+b(3)*exp(-b(6)*t) x0: [1 1 1 1 1 1] xdata: [3.9200 7.9300 11.8900 23.9000 47.8700 71.9100 93.8500 117.8400] ydata: [0.1630 0.6790 0.6790 0.3880 0.1830 0.1250 0.0860 0.0624] lb: [-10 -10 -10 0 0 0] ub: [10 10 10 0.5000 0.5000 0.5000] solver: 'lsqcurvefit' options: [1×1 optim.options.Lsqcurvefit]

## Solve the Problem

First solve the problem directly once.
b = lsqcurvefit(problem)
b = 1×6
0.1842 0.1836 0.1841 0.0172 0.0171 0.0171
You'll notice that the model does not do a stellar job fitting the data or even following the shape of the data.

## MultiStart

Let's see if we can do better by starting at a bunch of different points.
ms = MultiStart;
ms.Display = 'iter';
rng default
figure
tic
[~,fval,exitflag,output,solutions] = run(ms, problem, 50)
Run Local Local Local Local First-order Index exitflag f(x) # iter F-count optimality 1 3 0.222 8 63 0.0006396 2 3 0.000154 21 154 0.001864 3 3 0.009442 44 315 0.01989 4 3 1.462e-05 34 245 0.002586 5 3 1.454e-05 19 140 1.079e-05 6 3 1.475e-05 24 175 0.006883 7 3 0.009445 50 357 0.002266 8 3 1.495e-05 32 231 0.006853 9 3 1.466e-05 35 252 0.00478 10 3 0.00944 80 567 0.01042 11 3 1.471e-05 40 287 0.005472 12 3 1.566e-05 24 175 0.001576 13 3 0.009439 24 175 0.0005121 14 3 0.009451 41 294 0.02935 15 3 0.009493 26 189 0.004837 16 3 1.476e-05 40 287 0.006352 17 3 1.494e-05 40 287 0.008288 18 3 0.009446 62 441 0.01296 19 3 1.457e-05 22 161 0.001755 20 3 1.488e-05 53 378 0.007608 21 3 0.00944 37 266 0.006878 22 3 1.464e-05 24 175 0.003709 23 3 0.009449 43 308 0.02515 24 3 0.009447 47 336 0.007942 25 3 1.455e-05 23 168 0.001621 26 3 0.009442 32 231 0.01328 27 3 1.479e-05 40 287 0.004821 28 3 1.479e-05 18 133 0.006878 29 3 1.456e-05 72 511 0.0009721 30 3 1.455e-05 42 301 0.001122 31 3 0.009441 47 336 0.01537 32 3 0.009451 47 336 0.008942 33 3 0.0001729 14 105 0.0003276 34 3 0.009442 44 315 0.01062 35 3 0.0001751 21 154 7.71e-05 36 3 1.509e-05 26 189 0.009896 37 3 0.009458 39 280 0.02208 38 1 1.454e-05 24 175 7.815e-08 39 3 0.009441 60 427 0.0107 40 3 1.472e-05 34 245 0.002981 41 3 1.503e-05 22 161 0.00585 42 3 0.00952 15 112 0.008492 43 3 0.009439 21 154 0.000769 44 3 1.462e-05 64 455 0.0005576 45 3 0.009439 17 126 0.0001567 46 3 1.471e-05 30 217 0.001973 47 3 0.009444 38 273 0.02022 48 3 1.474e-05 24 175 0.004799 49 3 1.522e-05 42 301 0.008228
50 3 0.009445 40 287 0.02166 MultiStart completed the runs from all start points. All 50 local solver runs converged with a positive local solver exit flag.
fval = 1.4540e-05
exitflag = 1
output = struct with fields:
funcCount: 12726 localSolverTotal: 50 localSolverSuccess: 50 localSolverIncomplete: 0 localSolverNoSolution: 0 message: 'MultiStart completed the runs from all start points.↵↵All 50 local solver runs converged with a positive local solver exit flag.'
solutions = 1×50 object
123456789101112131415161718192021222324252627282930
11×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution1×1 GlobalOptimSolution
serialTime = toc;

## Visualize the Best Solution

The 50th solution, which is what is plotted above, is not necessarily the best one. Luckily for us, MultiStart orders the solutions from best to worst. So we need only look at the first one.
curvefittingPlotIterates(solutions)
You can see now that the 50th was not the best solution as the mean squared error on this final one displayed is over a factor of 10 better.

I will now see if we can improve the performance using all 4 of my cores as parallel workers locally.
ms.UseParallel = true;
gcp;
tic;
rng default
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 1 3 0.222 8 63 0.0006396 10 3 0.00944 80 567 0.01042 9 3 1.466e-05 35 252 0.00478 2 3 0.000154 21 154 0.001864 16 3 1.476e-05 40 287 0.006352 15 3 0.009493 26 189 0.004837 14 3 0.009451 41 294 0.02935 3 3 0.009442 44 315 0.01989 22 3 1.464e-05 24 175 0.003709 21 3 0.00944 37 266 0.006878 20 3 1.488e-05 53 378 0.007608 4 3 1.462e-05 34 245 0.002586 8 3 1.495e-05 32 231 0.006853 7 3 0.009445 50 357 0.002266 6 3 1.475e-05 24 175 0.006883 5 3 1.454e-05 19 140 1.079e-05 28 3 1.479e-05 18 133 0.006878 27 3 1.479e-05 40 287 0.004821 26 3 0.009442 32 231 0.01328 25 3 1.455e-05 23 168 0.001621 13 3 0.009439 24 175 0.0005121 12 3 1.566e-05 24 175 0.001576 11 3 1.471e-05 40 287 0.005472 34 3 0.009442 44 315 0.01062 33 3 0.0001729 14 105 0.0003276 32 3 0.009451 47 336 0.008942 19 3 1.457e-05 22 161 0.001755 18 3 0.009446 62 441 0.01296 17 3 1.494e-05 40 287 0.008288 24 3 0.009447 47 336 0.007942 23 3 0.009449 43 308 0.02515 31 3 0.009441 47 336 0.01537 30 3 1.455e-05 42 301 0.001122 29 3 1.456e-05 72 511 0.0009721 42 3 0.00952 15 112 0.008492 45 3 0.009439 17 126 0.0001567 37 3 0.009458 39 280 0.02208 36 3 1.509e-05 26 189 0.009896 35 3 0.0001751 21 154 7.71e-05 40 3 1.472e-05 34 245 0.002981 39 3 0.009441 60 427 0.0107 38 1 1.454e-05 24 175 7.815e-08 41 3 1.503e-05 22 161 0.00585 49 3 1.522e-05 42 301 0.008228 47 3 0.009444 38 273 0.02022 44 3 1.462e-05 64 455 0.0005576 43 3 0.009439 21 154 0.000769 48 3 1.474e-05 24 175 0.004799 50 3 0.009445 40 287 0.02166 46 3 1.471e-05 30 217 0.001973 MultiStart completed the runs from all start points. All 50 local solver runs converged with a positive local solver exit flag.
parallelTime = toc;

## Calculate Speedup

Speed up may not be evident until second run due to pool start up time. Since I started mine earlier, I get to see decent speed up.
speedup = serialTime/parallelTime
speedup = 2.5014

## Do You Have Problems Where the Solution is Sensitive to the Starting Point?

Tell us about your exploration of the solution space to your problem. If the solution is sensitive to where you start, you might consider using MultiStart and other techniques from the Global Optimization Toolbox.