Generate Code for lsqlin
Linear Least-Squares Problem to Solve
Create pseudorandom data for the problem of minimizing the norm of C*x –
d
subject to bounds and linear inequality constraints. Create a
problem for 15 variables, subject to the bounds lb = –1
and
ub = 1
and subject to 150 linear constraints A*x
<= b
.
N = 15; % Number of variables rng default % For reproducibility A = randn([10*N,N]); b = 5*ones(size(A,1),1); Aeq = []; % No equality constraints beq = []; ub = ones(N,1); lb = -ub; C = 10*eye(N) + randn(N); C = (C + C.')/2; % Symmetrize the matrix d = 20*randn(N,1);
Solve Using lsqlin
Code generation requires the "active-set"
algorithm, which
requires an initial point x0
. To solve the problem in MATLAB® using the algorithm required by code generation, set options and an
initial point. Also, set the UseCodegenSolver
option to
true
so you can verify the results in MATLAB using the same code as is generated.
x0 = zeros(size(d)); options = optimoptions("lsqlin",... Algorithm="active-set",UseCodegenSolver=true);
To solve the problem, call lsqlin
.
[x,fv,~,ef,output,lam] = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,options);
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.
After lsqlin
solves this problem, look at the number of
nonzero Lagrange multipliers of each type. See how many solution components are
unconstrained by subtracting the total number of nonzero Lagrange
multipliers.
nl = nnz(lam.lower);
nu = nnz(lam.upper);
ni = nnz(lam.ineqlin);
nunconstrained = N - nl - nu - ni;
fprintf('Number of solution components at lower bounds: %g\n',nl);
Number of solution components at lower bounds: 3
fprintf('Number of solution components at upper bounds: %g\n',nu);
Number of solution components at upper bounds: 2
fprintf('Number of solution components at inequality: %g\n',ni);
Number of solution components at inequality: 5
fprintf('Number of unconstrained solution components: %g\n',nunonstrained);
Number of unconstrained solution components: 5
Code Generation Steps
To solve the same problem using code generation, complete the following steps.
Write a function that incorporates all of the preceding steps. To produce less output, set the
Display
option to"off"
.function [x,fv,lam] = solvelsqlin N = 15; % Number of variables rng default % For reproducibility A = randn([10*N,N]); b = 5*ones(size(A,1),1); Aeq = []; % No equality constraints beq = []; ub = ones(N,1); lb = -ub; C = 10*eye(N) + randn(N); C = (C + C.')/2; % Symmetrize the matrix d = 20*randn(N,1); x0 = zeros(size(d)); options = optimoptions("lsqlin",Algorithm="active-set",... Display="off",UseCodegenSolver=true); [x,fv,~,ef,output,lam] = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,options); nl = nnz(lam.lower); nu = nnz(lam.upper); ni = nnz(lam.ineqlin); nunconstrained = N - nl - nu - ni; fprintf("Number of solution components at lower bounds: %g\n",nl); fprintf("Number of solution components at upper bounds: %g\n",nu); fprintf("Number of solution components at inequality: %g\n",ni); fprintf("Number of unconstrained solution components: %g\n",nunconstrained); end
Create a configuration for code generation. In this case, use
"mex"
.cfg = coder.config("mex");
Generate code for the
solvelsqlin
function.codegen -config cfg solvelsqlin
Code generation successful.
Test the generated code by running the generated file, which is named
solvelsqlin_mex.mexw64
or similar.[x2,fv2,lam2] = solvelsqlin_mex;
Number of solution components at lower bounds: 3 Number of solution components at upper bounds: 2 Number of solution components at inequality: 5 Number of unconstrained solution components: 5
The two solutions have the same number of Lagrange multipliers of each type. Check the differences between the returned solutions.
disp([norm(x - x2), abs(fv - fv2)])
1.0e-12 * 0.0006 0.4547
The differences between the two solutions are negligible.
See Also
quadprog
| lsqlin
| codegen
(MATLAB Coder) | optimoptions