Shared Concepts and Topics 
Syntax 
The NLOPTIONS statement provides you with syntax to control aspects of the nonlinear optimizations in the GLIMMIX, HPMIXED, and TCALIS procedures.
The nonlinear optimization options are described in alphabetical order after Table 18.10, which summarizes the options by category. The notation used in describing the options is generic in the sense that denotes the vector of parameters for the optimization and is its th element. The objective function being minimized, its gradient vector, and its Hessian matrix are denoted as , , and , respectively. The gradient with respect to the th parameter is denoted as . Superscripts in parentheses denote the iteration count; for example, is the value of the objective function at iteration . In the mixed model procedures, the parameter vector might consist of fixed effects only, covariance parameters only, or fixed effects and covariance parameters. In the TCALIS procedure, consists of all independent parameters defined in the models and in the PARAMETERS statement.
Option 
Description 

Optimization 

determines the type of Hessian scaling 

specifies the start for approximated Hessian 

specifies the linesearch method 

specifies the linesearch precision 

specifies the iteration number for update restart 

determines the minimization technique 

determines the update technique 

Termination Criteria 

tunes an absolute function convergence criterion 

tunes an absolute function convergence criterion 

tunes the absolute gradient convergence criterion 

tunes the absolute parameter convergence criterion 

tunes the relative function convergence criterion 

tunes another relative function convergence criterion 

specifies the value used in FCONV, GCONV criterion 

tunes the relative gradient convergence criterion 

tunes another relative gradient convergence criterion 

specifies the maximum number of function calls 

specifies the maximum number of iterations 

specifies the upper limit seconds of CPU time 

specifies the minimum number of iterations 

specifies the relative parameter convergence criterion 

specifies the value used in XCONV criterion 

Step Length 

dampens steps in line search 

specifies the initial trust region radius 

specifies the maximum trust region radius 

Printed Output 

displays (almost) all printed output 

displays optimization history 

suppresses all printed output 

Covariance Matrix Tolerances 

absolute singularity for inertia 

relative M singularity for inertia 

relative V singularity for inertia 

Constraint Specifications 

range for active constraints 

LM tolerance for deactivating 

tolerance for dependent constraints 

Remote Monitoring 

specifies the fileref for remote monitoring 
specifies an absolute function convergence criterion. For minimization, termination requires . The default value of is the negative square root of the largest doubleprecision value, which serves only as a protection against overflows.
specifies an absolute function convergence criterion. For all techniques except NMSIMP, termination requires a small change of the function value in successive iterations:
The same formula is used for the NMSIMP technique, but is defined as the vertex with the lowest function value, and is defined as the vertex with the highest function value in the simplex. The default value is . The optional integer value specifies the number of successive iterations for which the criterion must be satisfied before the process can be terminated.
specifies an absolute gradient convergence criterion. Termination requires the maximum absolute gradient element to be small:
This criterion is not used by the NMSIMP technique. The default value is 1E5. The optional integer value specifies the number of successive iterations for which the criterion must be satisfied before the process can be terminated.
specifies an absolute parameter convergence criterion. For all techniques except NMSIMP, termination requires a small Euclidean distance between successive parameter vectors,
For the NMSIMP technique, termination requires either a small length of the vertices of a restart simplex,
or a small simplex size,
where the simplex size is defined as the L1 distance from the simplex vertex with the smallest function value to the other simplex points :
The default is 1E8 for the NMSIMP technique and otherwise. The optional integer value specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.
specifies an absolute singularity criterion for the computation of the inertia (number of positive, negative, and zero eigenvalues) of the Hessian and its projected forms. The default value is the square root of the smallest positive doubleprecision value.
specifies that the initial step length value for each line search (used by the QUANEW, CONGRA, or NEWRAP technique) cannot be larger than times the step length value used in the former iteration. If the DAMPSTEP option is specified but is not specified, the default is . The DAMPSTEP= option can prevent the linesearch algorithm from repeatedly stepping into regions where some objective functions are difficult to compute or where they could lead to floatingpoint overflows during the computation of objective functions and their derivatives. The DAMPSTEP= option can save timeconsuming function calls during the line searches of objective functions that result in very small steps.
specifies a relative function convergence criterion. For all techniques except NMSIMP, termination requires a small relative change of the function value in successive iterations,
where FSIZE is defined by the FSIZE= option. The same formula is used for the NMSIMP technique, but is defined as the vertex with the lowest function value, and is defined as the vertex with the highest function value in the simplex.
The default is , where FDIGITS is by default and is the machine precision. Some procedures, such as the GLIMMIX procedure, enable you to change the value with the FDIGITS= option in the PROC statement. The optional integer value specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.
specifies a second function convergence criterion. For all techniques except NMSIMP, termination requires a small predicted reduction,
of the objective function. The predicted reduction
is computed by approximating the objective function by the first two terms of the Taylor series and substituting the Newton step,
For the NMSIMP technique, termination requires a small standard deviation of the function values of the simplex vertices , ,
where . If there are boundary constraints active at , the mean and standard deviation are computed only for the unconstrained vertices. The default value is 1E for the NMSIMP technique and otherwise. The optional integer value specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.
specifies the FSIZE parameter of the relative function and relative gradient termination criteria. The default value is . For more details, see the FCONV= and GCONV= options.
specifies a relative gradient convergence criterion. For all techniques except CONGRA and NMSIMP, termination requires that the normalized predicted function reduction be small,
where FSIZE is defined by the FSIZE= option. For the CONGRA technique (where a reliable Hessian estimate is not available), the following criterion is used:
This criterion is not used by the NMSIMP technique. The default value is 1E8. The optional integer value specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.
specifies another relative gradient convergence criterion. For leastsquares problems and the TRUREG, LEVMAR, NRRIDG, and NEWRAP techniques, the following criterion of Browne (1982) is used:
This criterion is not used by the other techniques. The default value is . The optional integer value specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.
specifies the scaling version of the Hessian (or crossproduct Jacobian) matrix used in NRRIDG, TRUREG, LEVMAR, NEWRAP, or DBLDOG optimization.
If HS is not equal to 0, the first iteration and each restart iteration set the diagonal scaling matrix :
where are the diagonal elements of the Hessian (or crossproduct Jacobian). In every other iteration, the diagonal scaling matrix is updated depending on the HS option:
specifies that no scaling be done.
specifies the Moré (1978) scaling update:
specifies the Dennis, Gay, and Welsch (1981) scaling update:
specifies that be reset in each iteration:
In each scaling update, is the relative machine precision. The default value is HS=0. Scaling of the Hessian can be timeconsuming in the case where general linear constraints are active.
specifies how the initial estimate of the approximate Hessian is defined for the quasiNewton techniques QUANEW and DBLDOG. There are two alternatives:
If you do not use the specification, the initial estimate of the approximate Hessian is set to the Hessian at .
If you do use the specification, the initial estimate of the approximate Hessian is set to the multiple of the identity matrix .
By default, if you do not specify the option INHESSIAN=, the initial estimate of the approximate Hessian is set to the multiple of the identity matrix , where the scalar is computed from the magnitude of the initial gradient.
reduces the length of the first trial step during the line search of the first iterations. For highly nonlinear objective functions, such as the EXP function, the default initial radius of the trustregion algorithm TRUREG or DBLDOG or the default step length of the linesearch algorithms can result in arithmetic overflows. If this occurs, you should specify decreasing values of such as INSTEP=1E1, INSTEP=1E2, INSTEP=1E4, and so on, until the iteration starts successfully.
For trustregion algorithms (TRUREG, DBLDOG), the INSTEP= option specifies a factor for the initial radius of the trust region. The default initial trustregion radius is the length of the scaled gradient. This step corresponds to the default radius factor of .
For linesearch algorithms (NEWRAP, CONGRA, QUANEW), the INSTEP= option specifies an upper bound for the initial step length for the line search during the first five iterations. The default initial step length is .
For the NelderMead simplex algorithm, by using TECH=NMSIMP, the INSTEP= option defines the size of the start simplex.
specifies a threshold for the Lagrange multiplier that determines whether an active inequality constraint remains active or can be deactivated. For maximization, must be greater than zero; for minimization, must be smaller than zero. An active inequality constraint can be deactivated only if its Lagrange multiplier is less than the threshold value. The default value is
where "+" stands for maximization, "" stands for minimization, ABSGCONV is the value of the absolute gradient criterion, and is the maximum absolute element of the gradient or the projected gradient.
specifies the range for active and violated boundary constraints. If the point satisfies the condition
the constraint is recognized as an active constraint. Otherwise, the constraint is either an inactive inequality or a violated inequality or equality constraint. The default value is 1E8. During the optimization process, the introduction of rounding errors can force the optimization to increase the value of by a factor of . If this happens, it is indicated by a message displayed in the log.
specifies a criterion , used in the update of the QR decomposition, that determines whether an active constraint is linearly dependent on a set of other active constraints. The default value is 1E8. The larger becomes, the more the active constraints are recognized as being linearly dependent. If the value of is larger than , it is reset to .
specifies the linesearch method for the CONGRA, QUANEW, and NEWRAP optimization techniques. See Fletcher (1987) for an introduction to linesearch techniques. The value of can be . For CONGRA, QUANEW, and NEWRAP, the default value is .
specifies a linesearch method that needs the same number of function and gradient calls for cubic interpolation and cubic extrapolation; this method is similar to one used by the Harwell subroutine library.
specifies a linesearch method that needs more function than gradient calls for quadratic and cubic interpolation and cubic extrapolation; this method is implemented as shown in Fletcher (1987) and can be modified to an exact line search by using the LSPRECISION= option.
specifies a linesearch method that needs the same number of function and gradient calls for cubic interpolation and cubic extrapolation; this method is implemented as shown in Fletcher (1987) and can be modified to an exact line search by using the LSPRECISION= option.
specifies a linesearch method that needs the same number of function and gradient calls for stepwise extrapolation and cubic interpolation.
specifies a linesearch method that is a modified version of LIS=4.
specifies golden section line search (Polak 1971), which uses only function values for linear approximation.
specifies bisection line search (Polak 1971), which uses only function values for linear approximation.
specifies the Armijo linesearch technique (Polak 1971), which uses only function values for linear approximation.
specifies the degree of accuracy that should be obtained by the linesearch algorithms LIS=2 and LIS=3. Usually an imprecise line search is inexpensive and successful. For more difficult optimization problems, a more precise and expensive line search might be necessary (Fletcher 1987). The second linesearch method (which is the default for the NEWRAP, QUANEW, and CONGRA techniques) and the third linesearch method approach exact line search for small LSPRECISION= values. If you have numerical problems, you should try to decrease the LSPRECISION= value to obtain a more precise line search. The default values are shown in Table 18.11.
TECH= 
UPDATE= 
LSP Default 

QUANEW 
DBFGS, BFGS 
= 0.4 
QUANEW 
DDFP, DFP 
= 0.06 
CONGRA 
all 
= 0.1 
NEWRAP 
no update 
= 0.9 
For more details, see Fletcher (1987).
specifies the maximum number of function calls in the optimization process. The default values are as follows:
TRUREG, NRRIDG, NEWRAP, LEVMAR: 125
QUANEW, DBLDOG: 500
CONGRA: 1000
NMSIMP: 3000
Note that the optimization can terminate only after completing a full iteration. Therefore, the number of function calls that are actually performed can exceed the number that is specified by the MAXFUNC= option.
specifies the maximum number of iterations in the optimization process. The default values are as follows:
TRUREG, NRRIDG, NEWRAP, LEVMAR: 50
QUANEW, DBLDOG: 200
CONGRA: 400
NMSIMP: 1000
These default values are also valid when is specified as a missing value.
specifies an upper bound for the step length of the linesearch algorithms during the first iterations. By default, is the largest doubleprecision value and is the largest integer available. Setting this option can improve the speed of convergence for the CONGRA, QUANEW, and NEWRAP techniques.
specifies an upper limit of seconds of CPU time for the optimization process. The default value is the largest floatingpoint double representation of your computer. Note that the time specified by the MAXTIME= option is checked only once at the end of each iteration. Therefore, the actual running time can be much longer than that specified by the MAXTIME= option. The actual running time includes the rest of the time needed to finish the iteration and the time needed to generate the output of the results.
specifies the minimum number of iterations. The default value is 0. If you request more iterations than are actually needed for convergence to a stationary point, the optimization algorithms can behave strangely. For example, the effect of rounding errors can prevent the algorithm from continuing for the required number of iterations.
specifies a relative singularity criterion for the computation of the inertia (number of positive, negative, and zero eigenvalues) of the Hessian and its projected forms. The default value is 1E12.
suppresses output related to optimization such as the iteration history. The GLIMMIX and HPMIXED procedures do not support printing options in the NLOPTIONS statement.
displays all optional output for optimization. This option is supported only by the TCALIS procedure.
displays the optimization history. The PHISTORY option is implied if the PALL option is specified. The PHISTORY option is supported only by the TCALIS procedure.
specifies that the QUANEW or CONGRA algorithm is restarted with a steepest descent/ascent search direction after, at most, iterations. Default values are as follows:
specifies the singularity criterion , , that is used for the inversion of the Hessian matrix. The default value is 1E8.
specifies the fileref that contains the information needed for remote monitoring. See the section Remote Monitoring for more details.
specifies the optimization technique. You can find additional information about choosing an optimization technique in the section Choosing an Optimization Algorithm. Valid values for the TECHNIQUE= option are as follows:
CONGRA
performs a conjugategradient optimization, which can be more precisely specified with the UPDATE= option and modified with the LINESEARCH= option. When you specify this option, UPDATE=PB by default.
DBLDOG
performs a version of doubledogleg optimization, which can be more precisely specified with the UPDATE= option. When you specify this option, UPDATE=DBFGS by default.
LEVMAR
performs a highly stable but, for large problems, memory and timeconsuming LevenbergMarquardt optimization technique, a slightly improved variant of the Moré (1978) implementation. You can also specify this technique with the alias LM or MARQUARDT. In the TCALIS procedure, this is the default optimization technique if there are fewer than 40 parameters to estimate. The GLIMMIX and HPMIXED procedures do not support this optimization technique.
NMSIMP
performs a NelderMead simplex optimization. The TCALIS procedure does not support this optimization technique.
NONE
does not perform any optimization. This option can be used for the following:
to perform a grid search without optimization
to compute estimates and predictions that cannot be obtained efficiently with any of the optimization techniques
to obtain inferences for known values of the covariance parameters
NEWRAP
performs a NewtonRaphson optimization combining a linesearch algorithm with ridging. The linesearch algorithm LIS=2 is the default method.
QUANEW
performs a quasiNewton optimization, which can be defined more precisely with the UPDATE= option and modified with the LINESEARCH= option.
TRUREG
performs a trustregion optimization.
specifies the update method for the quasiNewton, doubledogleg, or conjugategradient optimization technique. Not every update method can be used with each optimizer.
The following are the valid methods for the UPDATE= option:
BFGS
performs the original Broyden, Fletcher, Goldfarb, and Shanno (BFGS) update of the inverse Hessian matrix.
DBFGS
performs the dual BFGS update of the Cholesky factor of the Hessian matrix. This is the default update method.
DDFP
performs the dual Davidon, Fletcher, and Powell (DFP) update of the Cholesky factor of the Hessian matrix.
DFP
performs the original DFP update of the inverse Hessian matrix.
PB
performs the automatic restart update method of Powell (1977) and Beale (1972).
FR
performs the FletcherReeves update (Fletcher 1987).
PR
performs the PolakRibiere update (Fletcher 1987).
specifies the version of the quasiNewton optimization technique with nonlinear constraints.
specifies the update of the vector as in Powell (1978a, 1978b) (update like VF02AD).
specifies the update of the vector as in Powell (1982a, 1982b) (update like VMCWD).
The default is VERSION=2.
specifies a relative singularity criterion for the computation of the inertia (number of positive, negative, and zero eigenvalues) of the Hessian and its projected forms. The default value is E8.
specifies the relative parameter convergence criterion. For all techniques except NMSIMP, termination requires a small relative parameter change in subsequent iterations:
For the NMSIMP technique, the same formula is used, but is defined as the vertex with the lowest function value and is defined as the vertex with the highest function value in the simplex.
The default value is E8 for the NMSIMP technique and otherwise. The optional integer value specifies the number of successive iterations for which the criterion must be satisfied before the process can be terminated.
specifies the XSIZE parameter of the relative parameter termination criterion, . The default value is . For more details, see the XCONV= option.
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