PROC OPTMODEL provides a language for concisely modeling nonlinear programming (NLP) problems. The language allows a model to be expressed in a form that matches the mathematical formulation. Within OPTMODEL you can declare a model, pass it directly to various solvers, and review the solver result. For more details, refer to Chapter 8, The OPTMODEL Procedure.

You can solve the following types of nonlinear programming problems using PROC OPTMODEL:

• Nonlinear objective function, linear constraints: Invoke the constrained nonlinear programming (NLPC) solver. For more details about the NLPC solver, refer to Chapter 12, The NLPC Nonlinear Optimization Solver.

• Nonlinear objective function, nonlinear constraints: Invoke the sequential programming (SQP) or interior point nonlinear programming (IPNLP) solver. For more details about the SQP solver, refer to Chapter 15, The Sequential Quadratic Programming Solver. For more details about the IPNLP solver, refer to Chapter 9, The Interior Point NLP Solver.

• Nonlinear objective function, no constraints: Invoke the unconstrained nonlinear programming (NLPU) solver. For more details about the NLPU solver, refer to Chapter 13, The Unconstrained Nonlinear Programming Solver.