Documentation

Step 1: Load the Hessian and define f, lb, and ub.

load qpbox1   % Get H
lb = zeros(400,1); lb(400) = -inf;
ub = 0.9*ones(400,1); ub(400) = inf;
f = zeros(400,1); f([1 400]) = -2;

Step 2: Call a quadratic minimization routine with a starting point xstart.

xstart = 0.5*ones(400,1);
options = optimoptions('quadprog','Algorithm','trust-region-reflective');
[x,fval,exitflag,output] = ... 
        quadprog(H,f,[],[],[],[],lb,ub,xstart,options);

Looking at the resulting values of exitflag, output.iterations, and output.cgiterations,

exitflag,output.iterations,output.cgiterations

exitflag =

     3


ans =

    19


ans =

        1696

You can see that while convergence occurred in 19 iterations, the high number of CG iterations indicates that the cost of solving the linear system is high. In light of this cost, try using a direct solver at each iteration by setting the SubproblemAlgorithm option to 'factorization':

options = optimoptions(options,'SubproblemAlgorithm','factorization');
[x,fval,exitflag,output] = ... 
        quadprog(H,f,[],[],[],[],lb,ub,xstart,options);

Now the number of iterations has dropped to 10:

exitflag,output.iterations,output.cgiterations

exitflag =

     3


ans =

    10


ans =

     0

Using a direct solver at each iteration usually causes the number of iterations to decrease, but often takes more time per iteration. For this problem, the tradeoff is beneficial, as the time for quadprog to solve the problem decreases by a factor of 10.

You can also use the default 'interior-point-convex' algorithm to solve this convex problem:

options = optimoptions('quadprog','Algorithm','interior-point-convex');
[x,fval,exitflag,output] = ... 
        quadprog(H,f,[],[],[],[],lb,ub,[],options);

Check the exit flag and iterations (the interior-point algorithm does not use CG iterations):

exitflag,output.iterations

exitflag =

     1


ans =

     8