coneprog supports code generation using either the codegen (MATLAB Coder) 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.

Code generation targets do not use the same math kernel libraries as MATLAB solvers. Therefore, code generation solutions can vary from solver solutions, especially for poorly conditioned problems.

coneprog does not support the problem argument for code generation.

[x,fval] = coneprog(problem) % Not supported

All coneprog input matrices such as A, Aeq, lb, and ub must be full, not sparse. You can convert sparse matrices to full by using the full function.

Pass cone constraints as a cell array of SecondOrderConeConstraint objects, not as an array of these objects. If you have no cone constraints, pass an empty cell {}.

The lb and ub arguments must have the same number of entries as the number of problem variables or must be empty [].

If your target hardware does not support infinite bounds, use optim.coder.infbound.

For advanced code optimization involving embedded processors, you also need an Embedded Coder^® license.

Code generation supports these options using optimoptions:

Generated code has limited error checking for options. The recommended way to update an option is to use dot notation, not optimoptions.

opts = optimoptions("coneprog",ConstraintTolerance=1e-4);
opts = optimoptions(opts,MaxIterations=1e4); % Not supported
opts.MaxIterations = 1e4; % Recommended

coneprog supports options passed into the solver, not only options created in the 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.