Aliases: nloptr.print.options
### ** 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 equality 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.
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 equality 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.
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.