The NLP Procedure

Functional Summary

The following table outlines the options in PROC NLP classified by function. An alphabetical list of options is provided in the Dictionary of Options.

Table 7.1: Functional Summary

Description	Statement	Option
Input Data Set Options:
Input data set	PROC NLP	DATA=
Initial values and constraints	PROC NLP	INEST=
Quadratic objective function	PROC NLP	INQUAD=
Program statements	PROC NLP	MODEL=
Skip missing value observations	PROC NLP	NOMISS
Output Data Set Options:
Variables and derivatives	PROC NLP	OUT=
Result parameter values	PROC NLP	OUTEST=
Program statements	PROC NLP	OUTMODEL=
Combine various OUT…statements	PROC NLP	OUTALL
CRP Jacobian in the OUTEST= data set	PROC NLP	OUTCRPJAC
Derivatives in the OUT= data set	PROC NLP	OUTDER=
Grid in the OUTEST= data set	PROC NLP	OUTGRID
Hessian in the OUTEST= data set	PROC NLP	OUTHESSIAN
Iterative output in the OUTEST= data set	PROC NLP	OUTITER
Jacobian in the OUTEST= data set	PROC NLP	OUTJAC
NLC Jacobian in the OUTEST= data set	PROC NLP	OUTNLCJAC
Time in the OUTEST= data set	PROC NLP	OUTTIME
Optimization Options:
Minimization method	PROC NLP	TECH=
Update technique	PROC NLP	UPDATE=
Version of optimization technique	PROC NLP	VERSION=
Line-search method	PROC NLP	LINESEARCH=
Line-search precision	PROC NLP	LSPRECISION=
Type of Hessian scaling	PROC NLP	HESCAL=
Start for approximated Hessian	PROC NLP	INHESSIAN=
Iteration number for update restart	PROC NLP	RESTART=
Initial Value Options:
Produce best grid points	PROC NLP	BEST=
Infeasible points in grid search	PROC NLP	INFEASIBLE
Pseudorandom initial values	PROC NLP	RANDOM=
Constant initial values	PROC NLP	INITIAL=
Derivative Options:
Finite-difference derivatives	PROC NLP	FD=
Finite-difference derivatives	PROC NLP	FDHESSIAN=
Compute finite-difference interval	PROC NLP	FDINT=
Use only diagonal of Hessian	PROC NLP	DIAHES
Test gradient specification	PROC NLP	GRADCHECK=
Constraint Options:
Range for active constraints	PROC NLP	LCEPSILON=
LM tolerance for deactivating	PROC NLP	LCDEACT=
Tolerance for dependent constraints	PROC NLP	LCSINGULAR=
Sum all observations for continuous functions	NLINCON	/ SUMOBS
Evaluate each observation for continuous functions	NLINCON	/ EVERYOBS
Termination Criteria Options:
Maximum number of function calls	PROC NLP	MAXFUNC=
Maximum number of iterations	PROC NLP	MAXITER=
Minimum number of iterations	PROC NLP	MINITER=
Upper limit on real time	PROC NLP	MAXTIME=
Absolute function convergence criterion	PROC NLP	ABSCONV=
Absolute function convergence criterion	PROC NLP	ABSFCONV=
Absolute gradient convergence criterion	PROC NLP	ABSGCONV=
Absolute parameter convergence criterion	PROC NLP	ABSXCONV=
Relative function convergence criterion	PROC NLP	FCONV=
Relative function convergence criterion	PROC NLP	FCONV2=
Relative gradient convergence criterion	PROC NLP	GCONV=
Relative gradient convergence criterion	PROC NLP	GCONV2=
Relative parameter convergence criterion	PROC NLP	XCONV=
Used in FCONV, GCONV criterion	PROC NLP	FSIZE=
Used in XCONV criterion	PROC NLP	XSIZE=
Covariance Matrix Options:
Type of covariance matrix	PROC NLP	COV=
$\sigma ^2$ factor of COV matrix	PROC NLP	SIGSQ=
Determine factor of COV matrix	PROC NLP	VARDEF=
Absolute singularity for inertia	PROC NLP	ASINGULAR=
Relative M singularity for inertia	PROC NLP	MSINGULAR=
Relative V singularity for inertia	PROC NLP	VSINGULAR=
Threshold for Moore-Penrose inverse	PROC NLP	G4=
Tolerance for singular COV matrix	PROC NLP	COVSING=
Profile confidence limits	PROC NLP	CLPARM=
Printed Output Options:
Display (almost) all printed output	PROC NLP	PALL
Suppress all printed output	PROC NLP	NOPRINT
Reduce some default output	PROC NLP	PSHORT
Reduce most default output	PROC NLP	PSUMMARY
Display initial values and gradients	PROC NLP	PINIT
Display optimization history	PROC NLP	PHISTORY
Display Jacobian matrix	PROC NLP	PJACOBI
Display crossproduct Jacobian matrix	PROC NLP	PCRPJAC
Display Hessian matrix	PROC NLP	PHESSIAN
Display Jacobian of nonlinear constraints	PROC NLP	PNLCJAC
Display values of grid points	PROC NLP	PGRID
Display values of functions in LSQ, MIN, MAX	PROC NLP	PFUNCTION
Display approximate standard errors	PROC NLP	PSTDERR
Display covariance matrix	PROC NLP	PCOV
Display eigenvalues for covariance matrix	PROC NLP	PEIGVAL
Print code evaluation problems	PROC NLP	PERROR
Print measures of real time	PROC NLP	PTIME
Display model program, variables	PROC NLP	LIST
Display compiled model program	PROC NLP	LISTCODE
Step Length Options:
Damped steps in line search	PROC NLP	DAMPSTEP=
Maximum trust region radius	PROC NLP	MAXSTEP=
Initial trust region radius	PROC NLP	INSTEP=
Profile Point and Confidence Interval Options:
Factor relating discrepancy function to $\chi ^2$ quantile	PROFILE	FFACTOR=
Scale for $y$ values written to OUTEST= data set	PROFILE	FORCHI=
Upper bound for confidence limit search	PROFILE	FEASRATIO=
Write all confidence limit parameter estimates to OUTEST= data set	PROFILE	OUTTABLE
Miscellaneous Options:
Number of accurate digits in objective function	PROC NLP	FDIGITS=
Number of accurate digits in nonlinear constraints	PROC NLP	CDIGITS=
General singularity criterion	PROC NLP	SINGULAR=
Do not compute inertia of matrices	PROC NLP	NOEIGNUM
Check optimality in neighborhood	PROC NLP	OPTCHECK=