coneprog — Solve linear conic optimization problems with second-order cone constraints, linear constraints, equalities, and bounds.

coneprog minimizes f' * x subject to second-order cone constraints, optional linear inequalities, optional equalities, and optional bounds. RunMat supports positional forms, problem structs, optimoptions("coneprog"), and one- through five-output forms.

Syntax

[x, fval, exitflag] = coneprog(f, soc, A, b)

How coneprog works

• socConstraints may be [], a secondordercone struct, a cell array of cone structs, or an output list of cone structs.
• exitflag is 1 for solved, -2 for infeasible, and -3 for plainly unbounded.
• The output struct includes iterations, algorithm, constrviolation, and message.
• The lambda output contains ineqlin, eqlin, lower, upper, and soc multiplier estimates from the active constraint KKT system.
• GPU-resident numeric inputs are gathered because conic optimization is a host solver boundary.

Example

Solve a bounded cone problem

soc = secondordercone(eye(2), [0; 0], [0; 0], 1);
f = [-1; 0];
A = [1 0];
b = 0.75;
[x, fval, exitflag] = coneprog(f, soc, A, b)

Expected output:

x =
    0.7500
         0
fval =
   -0.7500
exitflag =
     1

Using coneprog with coding agents

Open a RunMat example with live inputs, then ask the agent to explain how coneprog changes the result.

FAQ

Related Math functions

Open-source implementation

Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how coneprog is executed, line by line, in Rust.

• View the source for coneprog in Rust on GitHub
• Learn how the RunMat runtime works
• Found a bug? Open an issue with a minimal reproduction.

About RunMat

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