Code generation is the conversion of MATLAB® code to C/C++ code using MATLAB Coder™. Code generation requires a MATLAB Coder license.
Typically, you use code generation to deploy code on hardware that is not running MATLAB. For an example, see Generate Code for quadprog. For general nonlinear programming, see Code Generation for Optimization Basics.
quadprog supports code generation using either the
codegen function or the MATLAB
Coder app. You must have a MATLAB
Coder license to generate code.
The target hardware must support standard double-precision floating-point computations. You cannot generate code for single-precision or fixed-point computations.
quadprog does not support the
problem argument for code
[x,fval] = quadprog(problem) % Not supported
quadprog input matrices such as
ub must be
full, not sparse. You can convert sparse matrices to full by using the
ub arguments must have the same
number of entries as the number of columns in
H or must be empty
For advanced code optimization involving embedded processors, you also need an Embedded Coder® license.
You must include options for
quadprog and specify them using
optimoptions. The options must include the
Algorithm option, set to
options = optimoptions('quadprog','Algorithm','active-set'); [x,fval,exitflag] = quadprog(H,f,A,b,Aeq,beq,lb,ub,x0,options);
Code generation supports these options:
Algorithm — Must be
Generated code has limited error checking for options. The recommended way to update
an option is to use
optimoptions, not dot notation.
opts = optimoptions('quadprog','Algorithm','active-set'); opts = optimoptions(opts,'MaxIterations',1e4); % Recommended opts.MaxIterations = 1e4; % Not recommended
Do not load options from a file. Doing so can cause code generation to fail. Instead, create options in your code.
If you specify an option that is not supported, the option is typically ignored during code generation. For reliable results, specify only supported options.
By default, generated code for use outside the MATLAB environment uses linear algebra libraries that are not multithreaded. Therefore, this code can run significantly slower than code in the MATLAB environment.
If your target hardware has multiple cores, then you can achieve better performance by using custom multithreaded LAPACK and BLAS libraries. To incorporate these libraries in your generated code, see Speed Up Linear Algebra in Generated Standalone Code by Using LAPACK Calls (MATLAB Coder).