The Nonlinear Programming Solver
- Overview
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Getting Started
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Syntax
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Details
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ExamplesSolving Highly Nonlinear Optimization ProblemsSolving Unconstrained and Bound-Constrained Optimization ProblemsSolving NLP Problems with Range ConstraintsSolving Large-Scale NLP ProblemsSolving NLP Problems That Have Several Local MinimaMaximum Likelihood Weibull EstimationFinding an Irreducible Infeasible Set
- References
Getting Started: NLP Solver
The NLP solver consists of two techniques that can solve a wide class of optimization problems efficiently and robustly. In this section two examples that introduce the two techniques of NLP are presented. The examples also introduce basic features of the modeling language of PROC OPTMODEL that is used to define the optimization problem.
The NLP solver can be invoked using the SOLVE statement,
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SOLVE WITH NLP </ options>;
where options specify the technique name, termination criteria, and how to display the results in the iteration log. For a detailed description of the options, see the section NLP Solver Options.