cuopt-numerical-optimization-api-c

Solve linear programming and MILP problems in C by using the cuOpt C API to create formulations, configure solver settings, execute cuOptSolve, and retrieve objective values and primal solutions. Build constraint matrices efficiently in CSR format alongside explicit variable bounds and continuous or integer variable typing.

Yes, GPU acceleration handles mixed-integer linear programming using sparse constraint matrices modeled in CSR format. This approach supports explicit variable bounds and variable typing for continuous or integer variables, enabling near real-time optimization on large problem instances directly within C applications.

The cuOpt C API supports quadratic programming in beta with specific limitations: it requires MINIMIZE objectives and continuous variables only. You can formulate convex-style quadratic objectives alongside sparse CSR constraints, but integer variables and maximization senses are not supported for QP problems.

The workflow to retrieve primal solutions after solving an LP in C involves creating the problem with cuOpt C headers, configuring solver settings, calling cuOptSolve, and then using cuOptGetObjectiveValue and cuOptGetPrimalSolution. You must validate formulation details including sense and variable types before extracting results.

Quadratic programming limitations in the cuOpt C API include support for MINIMIZE objectives only and continuous variables exclusively. QP formulations are in beta, meaning mixed-integer quadratic programming is unavailable, and users must validate formulation details to ensure compliance with these convex-style restrictions.