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
In each iteration k, the (dual) quasi-Newton, hybrid quasi-Newton, conjugate gradient, and Newton-Raphson minimization techniques use iterative
line-search algorithms that try to optimize a linear, quadratic, or cubic approximation of f along a feasible descent search direction 
![\[ x^{(k+1)} = x^{(k)} + \alpha ^{(k)} s^{(k)} , \quad \alpha ^{(k)} > 0 \]](images/ormplpug_nlp0329.png){kind=small}
by computing an approximately optimal scalar 
.
Therefore, a line-search algorithm is an iterative process that optimizes a nonlinear function 
of one parameter (
) within each iteration k of the optimization technique, which itself tries to optimize a linear or quadratic approximation of the nonlinear objective
function 
of n parameters x. Since the outside iteration process is based only on the approximation of the objective function, the inside iteration of
the line-search algorithm does not have to be perfect. Usually, the choice of significantly reduces (in a minimization) the objective function. Criteria often used for termination of line-search algorithms
are the Goldstein conditions (refer to Fletcher (1987)).
Various line-search algorithms can be selected using the LINESEARCH= option. The line-search method LINESEARCH= 2 seems to be superior when function evaluation consumes significantly less computation time than gradient evaluation. Therefore, LINESEARCH= 2 is the default value for Newton-Raphson, (dual) quasi-Newton, and conjugate gradient optimizations.
A special default line-search algorithm for TECH=
HYQUAN is useful only for least squares problems and cannot be chosen by the LINESEARCH=
option. This method uses three columns of the 
Jacobian matrix, which for large m can require more memory than using the algorithms designated by LINESEARCH=
1 through LINESEARCH=
8.
The line-search methods LINESEARCH=
2 and LINESEARCH=
3 can be modified to exact line search by using the LSPRECISION=
option (specifying the 
parameter in Fletcher (1987)). The line-search methods LINESEARCH=
1, LINESEARCH=
2, and LINESEARCH=
3 satisfy the left-hand-side and right-hand-side Goldstein conditions (refer to Fletcher (1987)). When derivatives are available, the line-search methods LINESEARCH=
6, LINESEARCH=
7, and LINESEARCH=
8 try to satisfy the right-hand-side Goldstein condition; if derivatives are not available, these line-search algorithms use
only function calls.