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Objective and Nonlinear Constraints in the Same Function

This example shows how to avoid calling a function twice when it computes values for both the objective and constraints using the solver-based approach. To avoid calling a function twice using the problem-based approach, see Objective and Constraints Having a Common Function in Serial or Parallel, Problem-Based.

You typically use such a function in a simulation. Solvers such as fmincon evaluate the objective and nonlinear constraint functions separately. This evaluation is wasteful when you use the same calculation for both results.

To avoid wasting time, use a nested function to evaluate the objective and constraint functions only when needed, by retaining the values of time-consuming calculations. This approach avoids using global variables, while retaining intermediate results be retained and sharing them between the objective and constraint functions.

Note

Because of the way ga (Global Optimization Toolbox) calls nonlinear constraint functions, the technique in this example usually does not reduce the number of calls to the objective or constraint functions.

Step 1. Write a function that computes the objective and constraints.

For example, suppose computeall is the expensive (time-consuming) function called by both the objective function and the nonlinear constraint functions. Assume that you want to use fmincon as your optimizer.

Write a function that computes a portion of Rosenbrock’s function f1 and includes a nonlinear constraint c1 that keeps the solution in a disk of radius 1 around the origin. Rosenbrock’s function is

f(x)=100(x2x12)2+(1x1)2,

which has a unique minimum value of 0 at (1,1). See Solve a Constrained Nonlinear Problem, Solver-Based.

This example has no nonlinear equality constraint, so ceq1 = []. Add a pause(1) statement to simulate an expensive computation.

function [f1,c1,ceq1] = computeall(x)
    ceq1 = [];
    c1 = norm(x)^2 - 1;
    f1 = 100*(x(2) - x(1)^2)^2 + (1-x(1))^2;
    pause(1) % Simulate expensive computation
end

Save computeall.m as a file on your MATLAB® path.

Step 2. Embed the function in a nested function that keeps recent values.

Suppose the objective function is

y = 100(x2x12)2 + (1 – x1)2
+ 20*(x3x42)2 + 5*(1 – x4)2.

computeall returns the first part of the objective function. Embed the call to computeall in a nested function:

function [x,f,eflag,outpt] = runobjconstr(x0,opts)

if nargin == 1 % No options supplied
    opts = [];
end

xLast = []; % Last place computeall was called
myf = []; % Use for objective at xLast
myc = []; % Use for nonlinear inequality constraint
myceq = []; % Use for nonlinear equality constraint

fun = @objfun; % The objective function, nested below
cfun = @constr; % The constraint function, nested below

% Call fmincon
[x,f,eflag,outpt] = fmincon(fun,x0,[],[],[],[],[],[],cfun,opts);

    function y = objfun(x)
        if ~isequal(x,xLast) % Check if computation is necessary
            [myf,myc,myceq] = computeall(x);
            xLast = x;
        end
        % Now compute objective function
        y = myf + 20*(x(3) - x(4)^2)^2 + 5*(1 - x(4))^2;
    end

    function [c,ceq] = constr(x)
        if ~isequal(x,xLast) % Check if computation is necessary
            [myf,myc,myceq] = computeall(x);
            xLast = x;
        end
        % Now compute constraint function
        c = myc; % In this case, the computation is trivial
        ceq = myceq;
    end

end

Save the nested function as a file named runobjconstr.m on your MATLAB path.

Step 3. Determine the time to run with the nested function.

Run the function, timing the call with tic and toc.

opts = optimoptions(@fmincon,'Algorithm','interior-point','Display','off');
x0 = [-1,1,1,2];
tic
[x,fval,exitflag,output] = runobjconstr(x0,opts);
toc
Elapsed time is 203.797275 seconds.

Step 4. Determine the time to run without the nested function.

Compare the times to run the solver with and without the nested function. For the run without the nested function, save myrosen2.m as the objective function file and constr.m as the constraint.

function y = myrosen2(x)
    f1 = computeall(x); % Get first part of objective
    y = f1 + 20*(x(3) - x(4)^2)^2 + 5*(1 - x(4))^2;
end

function [c,ceq] = constr(x)
    [~,c,ceq] = computeall(x);
end

Run fmincon, timing the call with tic and toc.

tic
[x,fval,exitflag,output] = fmincon(@myrosen2,x0,...
                   [],[],[],[],[],[],@constr,opts);
toc
Elapsed time is 406.771978 seconds.

The solver takes twice as long as before, because it evaluates the objective and constraint separately.

Step 5. Save computing time with parallel computing.

If you have a Parallel Computing Toolbox™ license, you can save even more time by setting the UseParallel option to true.

parpool
Starting parallel pool (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
opts = optimoptions(opts,'UseParallel',true);
tic
[x,fval,exitflag,output] = runobjconstr(x0,opts);
toc
Elapsed time is 97.528110 seconds.

In this case, enabling parallel computing reduces the computational time in half, compared to the serial run with the nested function.

Compare the runs with parallel computing, with and without a nested function:

tic
[x,fval,exitflag,output] = fmincon(@myrosen2,x0,...
                   [],[],[],[],[],[],@constr,opts);
toc
Elapsed time is 188.985178 seconds.

In this example, computing in parallel but not nested takes about the same time as computing nested but not parallel. Computing both nested and parallel takes half the time of using either alone.

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