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 GRADIENT statement defines the gradient vector which contains the first-order derivatives of the objective function f with respect to 
. For more information, see the section Derivatives. To specify the values of nonzero derivatives, the variables specified in the GRADIENT statement must be defined on the left-hand
side of algebraic expressions in programming statements. For example, consider the Rosenbrock function:
proc nlp tech=congra; min y; decvar x1 x2; gradient g1 g2; y1 = 10 * (x2 - x1 * x1); y2 = 1 - x1; y = .5 * (y1 * y1 + y2 * y2); g1 = -200 * x1 * (x2 - x1 * x1) - (1 - x1); g2 = 100 * (x2 - x1 * x1); run;