Print description of nloptr options

This function prints a list of all the options that can be set when solving a minimization problem using nloptr.

Usage

nloptr.print.options(opts.show = NULL, opts.user = NULL)

Arguments

opts.show

list or vector with names of options. A description will be shown for the options in this list. By default, a description of all options is shown.

opts.user

object containing user supplied options. This argument is optional. It is used when nloptr.print.options is called from nloptr. In that case options are listed if print_options_doc is set to TRUE when passing a minimization problem to nloptr.

See also

nloptr

Author

Jelmer Ypma

Examples

library('nloptr')
nloptr.print.options()
#> algorithm
#> 	possible values: NLOPT_GN_DIRECT, NLOPT_GN_DIRECT_L,
#> 	         NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_NOSCAL,
#> 	         NLOPT_GN_DIRECT_L_NOSCAL,
#> 	         NLOPT_GN_DIRECT_L_RAND_NOSCAL,
#> 	         NLOPT_GN_ORIG_DIRECT, NLOPT_GN_ORIG_DIRECT_L,
#> 	         NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND,
#> 	         NLOPT_LD_SLSQP, NLOPT_LD_LBFGS_NOCEDAL,
#> 	         NLOPT_LD_LBFGS, NLOPT_LN_PRAXIS, NLOPT_LD_VAR1,
#> 	         NLOPT_LD_VAR2, NLOPT_LD_TNEWTON,
#> 	         NLOPT_LD_TNEWTON_RESTART,
#> 	         NLOPT_LD_TNEWTON_PRECOND,
#> 	         NLOPT_LD_TNEWTON_PRECOND_RESTART,
#> 	         NLOPT_GN_CRS2_LM, NLOPT_GN_MLSL, NLOPT_GD_MLSL,
#> 	         NLOPT_GN_MLSL_LDS, NLOPT_GD_MLSL_LDS,
#> 	         NLOPT_LD_MMA, NLOPT_LD_CCSAQ, NLOPT_LN_COBYLA,
#> 	         NLOPT_LN_NEWUOA, NLOPT_LN_NEWUOA_BOUND,
#> 	         NLOPT_LN_NELDERMEAD, NLOPT_LN_SBPLX,
#> 	         NLOPT_LN_AUGLAG, NLOPT_LD_AUGLAG,
#> 	         NLOPT_LN_AUGLAG_EQ, NLOPT_LD_AUGLAG_EQ,
#> 	         NLOPT_LN_BOBYQA, NLOPT_GN_ISRES
#> 	default value:   none
#> 
#> 	This option is required. Check the NLopt website for a description of
#> 	the algorithms.
#> 
#> stopval
#> 	possible values: -Inf <= stopval <= Inf
#> 	default value:   -Inf
#> 
#> 	Stop minimization when an objective value <= stopval is found.
#> 	Setting stopval to -Inf disables this stopping criterion (default).
#> 
#> ftol_rel
#> 	possible values: ftol_rel > 0
#> 	default value:   0.0
#> 
#> 	Stop when an optimization step (or an estimate of the optimum)
#> 	changes the objective function value by less than ftol_rel multiplied
#> 	by the absolute value of the function value. If there is any chance
#> 	that your optimum function value is close to zero, you might want to
#> 	set an absolute tolerance with ftol_abs as well. Criterion is
#> 	disabled if ftol_rel is non-positive (default).
#> 
#> ftol_abs
#> 	possible values: ftol_abs > 0
#> 	default value:   0.0
#> 
#> 	Stop when an optimization step (or an estimate of the optimum)
#> 	changes the function value by less than ftol_abs. Criterion is
#> 	disabled if ftol_abs is non-positive (default).
#> 
#> xtol_rel
#> 	possible values: xtol_rel > 0
#> 	default value:   1.0e-04
#> 
#> 	Stop when an optimization step (or an estimate of the optimum)
#> 	changes every parameter by less than xtol_rel multiplied by the
#> 	absolute value of the parameter. If there is any chance that an
#> 	optimal parameter is close to zero, you might want to set an absolute
#> 	tolerance with xtol_abs as well. Criterion is disabled if xtol_rel is
#> 	non-positive.
#> 
#> xtol_abs
#> 	possible values: xtol_abs > 0
#> 	default value:   rep(0.0, length(x0))
#> 
#> 	xtol_abs is a vector of length n (the number of elements in x) giving
#> 	the tolerances: stop when an optimization step (or an estimate of the
#> 	optimum) changes every parameter x[i] by less than xtol_abs[i].
#> 	Criterion is disabled if all elements of xtol_abs are non-positive
#> 	(default).
#> 
#> maxeval
#> 	possible values: maxeval is a positive integer
#> 	default value:   100
#> 
#> 	Stop when the number of function evaluations exceeds maxeval. This is
#> 	not a strict maximum: the number of function evaluations may exceed
#> 	maxeval slightly, depending upon the algorithm. Criterion is disabled
#> 	if maxeval is non-positive.
#> 
#> maxtime
#> 	possible values: maxtime > 0
#> 	default value:   -1.0
#> 
#> 	Stop when the optimization time (in seconds) exceeds maxtime. This is
#> 	not a strict maximum: the time may exceed maxtime slightly, depending
#> 	upon the algorithm and on how slow your function evaluation is.
#> 	Criterion is disabled if maxtime is non-positive (default).
#> 
#> tol_constraints_ineq
#> 	possible values: tol_constraints_ineq > 0.0
#> 	default value:   rep(1e-8, num_constraints_ineq)
#> 
#> 	The parameter tol_constraints_ineq is a vector of tolerances. Each
#> 	tolerance corresponds to one of the inequality constraints. The
#> 	tolerance is used for the purpose of stopping criteria only: a point
#> 	x is considered feasible for judging whether to stop the optimization
#> 	if eval_g_ineq(x) <= tol. A tolerance of zero means that NLopt will
#> 	try not to consider any x to be converged unless eval_g_ineq(x) is
#> 	strictly non-positive; generally, at least a small positive tolerance
#> 	is advisable to reduce sensitivity to rounding errors. By default the
#> 	tolerances for all inequality constraints are set to 1e-8.
#> 
#> tol_constraints_eq
#> 	possible values: tol_constraints_eq > 0.0
#> 	default value:   rep(1e-8, num_constraints_eq)
#> 
#> 	The parameter tol_constraints_eq is a vector of tolerances. Each
#> 	tolerance corresponds to one of the equality constraints. The
#> 	tolerance is used for the purpose of stopping criteria only: a point
#> 	x is considered feasible for judging whether to stop the optimization
#> 	if abs(eval_g_ineq(x)) <= tol. For equality constraints, a small
#> 	positive tolerance is strongly advised in order to allow NLopt to
#> 	converge even if the equality constraint is slightly nonzero. By
#> 	default the tolerances for all quality constraints are set to 1e-8.
#> 
#> print_level
#> 	possible values: 0, 1, 2, or 3
#> 	default value:   0
#> 
#> 	The option print_level controls how much output is shown during the
#> 	optimization process. Possible values: 0 (default): no output; 1:
#> 	show iteration number and value of objective function; 2: 1 + show
#> 	value of (in)equalities; 3: 2 + show value of controls.
#> 
#> check_derivatives
#> 	possible values: TRUE or FALSE
#> 	default value:   FALSE
#> 
#> 	The option check_derivatives can be activated to compare the
#> 	user-supplied analytic gradients with finite difference
#> 	approximations.
#> 
#> check_derivatives_tol
#> 	possible values: check_derivatives_tol > 0.0
#> 	default value:   1e-04
#> 
#> 	The option check_derivatives_tol determines when a difference between
#> 	an analytic gradient and its finite difference approximation is
#> 	flagged as an error.
#> 
#> check_derivatives_print
#> 	possible values: 'none', 'all', 'errors',
#> 	default value:   all
#> 
#> 	The option check_derivatives_print controls the output of the
#> 	derivative checker (if check_derivatives == TRUE). All comparisons
#> 	are shown ('all'), only those comparisions that resulted in an error
#> 	('error'), or only the number of errors is shown ('none').
#> 
#> print_options_doc
#> 	possible values: TRUE or FALSE
#> 	default value:   FALSE
#> 
#> 	If TRUE, a description of all options and their current and default
#> 	values is printed to the screen.
#> 
#> population
#> 	possible values: population is a positive integer
#> 	default value:   0
#> 
#> 	Several of the stochastic search algorithms (e.g., CRS, MLSL, and
#> 	ISRES) start by generating some initial population of random points
#> 	x. By default, this initial population size is chosen heuristically
#> 	in some algorithm-specific way, but the initial population can by
#> 	changed by setting a positive integer value for population. A
#> 	population of zero implies that the heuristic default will be used.
#> 
#> vector_storage
#> 	possible values: vector_storage is a positive integer
#> 	default value:   20
#> 
#> 	Number of gradients to remember from previous optimization steps.
#> 
#> ranseed
#> 	possible values: ranseed is a positive integer
#> 	default value:   0
#> 
#> 	For stochastic optimization algorithms, pseudorandom numbers are
#> 	generated. Set the random seed using ranseed if you want to use a
#> 	'deterministic' sequence of pseudorandom numbers, i.e. the same
#> 	sequence from run to run. If ranseed is 0 (default), the seed for the
#> 	random numbers is generated from the system time, so that you will
#> 	get a different sequence of pseudorandom numbers each time you run
#> 	your program.
#> 

nloptr.print.options(opts.show = c("algorithm", "check_derivatives"))
#> algorithm
#> 	possible values: NLOPT_GN_DIRECT, NLOPT_GN_DIRECT_L,
#> 	         NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_NOSCAL,
#> 	         NLOPT_GN_DIRECT_L_NOSCAL,
#> 	         NLOPT_GN_DIRECT_L_RAND_NOSCAL,
#> 	         NLOPT_GN_ORIG_DIRECT, NLOPT_GN_ORIG_DIRECT_L,
#> 	         NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND,
#> 	         NLOPT_LD_SLSQP, NLOPT_LD_LBFGS_NOCEDAL,
#> 	         NLOPT_LD_LBFGS, NLOPT_LN_PRAXIS, NLOPT_LD_VAR1,
#> 	         NLOPT_LD_VAR2, NLOPT_LD_TNEWTON,
#> 	         NLOPT_LD_TNEWTON_RESTART,
#> 	         NLOPT_LD_TNEWTON_PRECOND,
#> 	         NLOPT_LD_TNEWTON_PRECOND_RESTART,
#> 	         NLOPT_GN_CRS2_LM, NLOPT_GN_MLSL, NLOPT_GD_MLSL,
#> 	         NLOPT_GN_MLSL_LDS, NLOPT_GD_MLSL_LDS,
#> 	         NLOPT_LD_MMA, NLOPT_LD_CCSAQ, NLOPT_LN_COBYLA,
#> 	         NLOPT_LN_NEWUOA, NLOPT_LN_NEWUOA_BOUND,
#> 	         NLOPT_LN_NELDERMEAD, NLOPT_LN_SBPLX,
#> 	         NLOPT_LN_AUGLAG, NLOPT_LD_AUGLAG,
#> 	         NLOPT_LN_AUGLAG_EQ, NLOPT_LD_AUGLAG_EQ,
#> 	         NLOPT_LN_BOBYQA, NLOPT_GN_ISRES
#> 	default value:   none
#> 
#> 	This option is required. Check the NLopt website for a description of
#> 	the algorithms.
#> 
#> check_derivatives
#> 	possible values: TRUE or FALSE
#> 	default value:   FALSE
#> 
#> 	The option check_derivatives can be activated to compare the
#> 	user-supplied analytic gradients with finite difference
#> 	approximations.
#> 

opts <- list("algorithm"="NLOPT_LD_LBFGS",
       "xtol_rel"=1.0e-8)
nloptr.print.options(opts.user = opts)
#> algorithm
#> 	possible values: NLOPT_GN_DIRECT, NLOPT_GN_DIRECT_L,
#> 	         NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_NOSCAL,
#> 	         NLOPT_GN_DIRECT_L_NOSCAL,
#> 	         NLOPT_GN_DIRECT_L_RAND_NOSCAL,
#> 	         NLOPT_GN_ORIG_DIRECT, NLOPT_GN_ORIG_DIRECT_L,
#> 	         NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND,
#> 	         NLOPT_LD_SLSQP, NLOPT_LD_LBFGS_NOCEDAL,
#> 	         NLOPT_LD_LBFGS, NLOPT_LN_PRAXIS, NLOPT_LD_VAR1,
#> 	         NLOPT_LD_VAR2, NLOPT_LD_TNEWTON,
#> 	         NLOPT_LD_TNEWTON_RESTART,
#> 	         NLOPT_LD_TNEWTON_PRECOND,
#> 	         NLOPT_LD_TNEWTON_PRECOND_RESTART,
#> 	         NLOPT_GN_CRS2_LM, NLOPT_GN_MLSL, NLOPT_GD_MLSL,
#> 	         NLOPT_GN_MLSL_LDS, NLOPT_GD_MLSL_LDS,
#> 	         NLOPT_LD_MMA, NLOPT_LD_CCSAQ, NLOPT_LN_COBYLA,
#> 	         NLOPT_LN_NEWUOA, NLOPT_LN_NEWUOA_BOUND,
#> 	         NLOPT_LN_NELDERMEAD, NLOPT_LN_SBPLX,
#> 	         NLOPT_LN_AUGLAG, NLOPT_LD_AUGLAG,
#> 	         NLOPT_LN_AUGLAG_EQ, NLOPT_LD_AUGLAG_EQ,
#> 	         NLOPT_LN_BOBYQA, NLOPT_GN_ISRES
#> 	default value:   none
#> 	current value:   NLOPT_LD_LBFGS
#> 
#> 	This option is required. Check the NLopt website for a description of
#> 	the algorithms.
#> 
#> stopval
#> 	possible values: -Inf <= stopval <= Inf
#> 	default value:   -Inf
#> 	current value:   (default)
#> 
#> 	Stop minimization when an objective value <= stopval is found.
#> 	Setting stopval to -Inf disables this stopping criterion (default).
#> 
#> ftol_rel
#> 	possible values: ftol_rel > 0
#> 	default value:   0.0
#> 	current value:   (default)
#> 
#> 	Stop when an optimization step (or an estimate of the optimum)
#> 	changes the objective function value by less than ftol_rel multiplied
#> 	by the absolute value of the function value. If there is any chance
#> 	that your optimum function value is close to zero, you might want to
#> 	set an absolute tolerance with ftol_abs as well. Criterion is
#> 	disabled if ftol_rel is non-positive (default).
#> 
#> ftol_abs
#> 	possible values: ftol_abs > 0
#> 	default value:   0.0
#> 	current value:   (default)
#> 
#> 	Stop when an optimization step (or an estimate of the optimum)
#> 	changes the function value by less than ftol_abs. Criterion is
#> 	disabled if ftol_abs is non-positive (default).
#> 
#> xtol_rel
#> 	possible values: xtol_rel > 0
#> 	default value:   1.0e-04
#> 	current value:   1e-08
#> 
#> 	Stop when an optimization step (or an estimate of the optimum)
#> 	changes every parameter by less than xtol_rel multiplied by the
#> 	absolute value of the parameter. If there is any chance that an
#> 	optimal parameter is close to zero, you might want to set an absolute
#> 	tolerance with xtol_abs as well. Criterion is disabled if xtol_rel is
#> 	non-positive.
#> 
#> xtol_abs
#> 	possible values: xtol_abs > 0
#> 	default value:   rep(0.0, length(x0))
#> 	current value:   (default)
#> 
#> 	xtol_abs is a vector of length n (the number of elements in x) giving
#> 	the tolerances: stop when an optimization step (or an estimate of the
#> 	optimum) changes every parameter x[i] by less than xtol_abs[i].
#> 	Criterion is disabled if all elements of xtol_abs are non-positive
#> 	(default).
#> 
#> maxeval
#> 	possible values: maxeval is a positive integer
#> 	default value:   100
#> 	current value:   (default)
#> 
#> 	Stop when the number of function evaluations exceeds maxeval. This is
#> 	not a strict maximum: the number of function evaluations may exceed
#> 	maxeval slightly, depending upon the algorithm. Criterion is disabled
#> 	if maxeval is non-positive.
#> 
#> maxtime
#> 	possible values: maxtime > 0
#> 	default value:   -1.0
#> 	current value:   (default)
#> 
#> 	Stop when the optimization time (in seconds) exceeds maxtime. This is
#> 	not a strict maximum: the time may exceed maxtime slightly, depending
#> 	upon the algorithm and on how slow your function evaluation is.
#> 	Criterion is disabled if maxtime is non-positive (default).
#> 
#> tol_constraints_ineq
#> 	possible values: tol_constraints_ineq > 0.0
#> 	default value:   rep(1e-8, num_constraints_ineq)
#> 	current value:   (default)
#> 
#> 	The parameter tol_constraints_ineq is a vector of tolerances. Each
#> 	tolerance corresponds to one of the inequality constraints. The
#> 	tolerance is used for the purpose of stopping criteria only: a point
#> 	x is considered feasible for judging whether to stop the optimization
#> 	if eval_g_ineq(x) <= tol. A tolerance of zero means that NLopt will
#> 	try not to consider any x to be converged unless eval_g_ineq(x) is
#> 	strictly non-positive; generally, at least a small positive tolerance
#> 	is advisable to reduce sensitivity to rounding errors. By default the
#> 	tolerances for all inequality constraints are set to 1e-8.
#> 
#> tol_constraints_eq
#> 	possible values: tol_constraints_eq > 0.0
#> 	default value:   rep(1e-8, num_constraints_eq)
#> 	current value:   (default)
#> 
#> 	The parameter tol_constraints_eq is a vector of tolerances. Each
#> 	tolerance corresponds to one of the equality constraints. The
#> 	tolerance is used for the purpose of stopping criteria only: a point
#> 	x is considered feasible for judging whether to stop the optimization
#> 	if abs(eval_g_ineq(x)) <= tol. For equality constraints, a small
#> 	positive tolerance is strongly advised in order to allow NLopt to
#> 	converge even if the equality constraint is slightly nonzero. By
#> 	default the tolerances for all quality constraints are set to 1e-8.
#> 
#> print_level
#> 	possible values: 0, 1, 2, or 3
#> 	default value:   0
#> 	current value:   (default)
#> 
#> 	The option print_level controls how much output is shown during the
#> 	optimization process. Possible values: 0 (default): no output; 1:
#> 	show iteration number and value of objective function; 2: 1 + show
#> 	value of (in)equalities; 3: 2 + show value of controls.
#> 
#> check_derivatives
#> 	possible values: TRUE or FALSE
#> 	default value:   FALSE
#> 	current value:   (default)
#> 
#> 	The option check_derivatives can be activated to compare the
#> 	user-supplied analytic gradients with finite difference
#> 	approximations.
#> 
#> check_derivatives_tol
#> 	possible values: check_derivatives_tol > 0.0
#> 	default value:   1e-04
#> 	current value:   (default)
#> 
#> 	The option check_derivatives_tol determines when a difference between
#> 	an analytic gradient and its finite difference approximation is
#> 	flagged as an error.
#> 
#> check_derivatives_print
#> 	possible values: 'none', 'all', 'errors',
#> 	default value:   all
#> 	current value:   (default)
#> 
#> 	The option check_derivatives_print controls the output of the
#> 	derivative checker (if check_derivatives == TRUE). All comparisons
#> 	are shown ('all'), only those comparisions that resulted in an error
#> 	('error'), or only the number of errors is shown ('none').
#> 
#> print_options_doc
#> 	possible values: TRUE or FALSE
#> 	default value:   FALSE
#> 	current value:   (default)
#> 
#> 	If TRUE, a description of all options and their current and default
#> 	values is printed to the screen.
#> 
#> population
#> 	possible values: population is a positive integer
#> 	default value:   0
#> 	current value:   (default)
#> 
#> 	Several of the stochastic search algorithms (e.g., CRS, MLSL, and
#> 	ISRES) start by generating some initial population of random points
#> 	x. By default, this initial population size is chosen heuristically
#> 	in some algorithm-specific way, but the initial population can by
#> 	changed by setting a positive integer value for population. A
#> 	population of zero implies that the heuristic default will be used.
#> 
#> vector_storage
#> 	possible values: vector_storage is a positive integer
#> 	default value:   20
#> 	current value:   (default)
#> 
#> 	Number of gradients to remember from previous optimization steps.
#> 
#> ranseed
#> 	possible values: ranseed is a positive integer
#> 	default value:   0
#> 	current value:   (default)
#> 
#> 	For stochastic optimization algorithms, pseudorandom numbers are
#> 	generated. Set the random seed using ranseed if you want to use a
#> 	'deterministic' sequence of pseudorandom numbers, i.e. the same
#> 	sequence from run to run. If ranseed is 0 (default), the seed for the
#> 	random numbers is generated from the system time, so that you will
#> 	get a different sequence of pseudorandom numbers each time you run
#> 	your program.
#>