The NLP Procedure
- Overview
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Getting Started
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SyntaxFunctional SummaryDictionary of OptionsPROC NLP StatementARRAY StatementBOUNDS StatementBY StatementCRPJAC StatementDECVAR StatementGRADIENT StatementHESSIAN StatementINCLUDE StatementJACNLC StatementJACOBIAN StatementLABEL StatementLINCON StatementMATRIX StatementMIN, MAX, and LSQ StatementsMINQUAD and MAXQUAD StatementsNLINCON StatementPROFILE StatementProgram Statements
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DetailsCriteria for OptimalityOptimization AlgorithmsFinite-Difference Approximations of DerivativesHessian and CRP Jacobian ScalingTesting the Gradient SpecificationTermination CriteriaActive Set MethodsFeasible Starting PointLine-Search MethodsRestricting the Step LengthComputational ProblemsCovariance MatrixInput and Output Data SetsDisplayed OutputMissing ValuesComputational ResourcesMemory LimitRewriting NLP Models for PROC OPTMODEL
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Examples
- References
The NLP procedure solves general nonlinear programs. It has several optimizers that are tuned to best perform on a particular class of problems. Guidelines for choosing a particular optimizer for a problem can be found in the section Optimization Algorithms.
Regardless of the selected optimizer, it is necessary to specify an objective function and constraints that the optimal solution must satisfy. In PROC NLP, the objective function and the constraints are specified using SAS programming statements that are similar to those used in the SAS DATA step. Some of the differences are discussed in the section Program Statements and in the section ARRAY Statement. As with any programming language, there are many different ways to specify the same problem. Some are more economical than others.