NLopt solvers#
#include <pagmo/algorithms/nlopt.hpp>

class nlopt : public pagmo::not_population_based#
NLopt algorithms.
This userdefined algorithm wraps a selection of solvers from the NLopt library, focusing on local optimisation (both gradientbased and derivativefree). The complete list of supported NLopt algorithms is:
COBYLA,
BOBYQA,
NEWUOA + bound constraints,
PRAXIS,
NelderMead simplex,
sbplx,
MMA (Method of Moving Asymptotes),
CCSA,
SLSQP,
lowstorage BFGS,
preconditioned truncated Newton,
shifted limitedmemory variablemetric,
augmented Lagrangian algorithm.
The desired NLopt solver is selected upon construction of a pagmo::nlopt algorithm. Various properties of the solver (e.g., the stopping criteria) can be configured after construction via methods provided by this class. Multiple stopping criteria can be active at the same time: the optimisation will stop as soon as at least one stopping criterion is satisfied. By default, only the
xtol_rel
stopping criterion is active (see get_xtol_rel()).All NLopt solvers support only singleobjective optimisation, and, as usual in pagmo, minimisation is always assumed. The gradientbased algorithms require the optimisation problem to provide a gradient. Some solvers support equality and/or inequality constraints. The constraints’ tolerances will be set to those specified in the pagmo::problem being optimised (see pagmo::problem::set_c_tol()).
In order to support pagmo’s populationbased optimisation model, nlopt::evolve() will select a single individual from the input pagmo::population to be optimised by the NLopt solver. If the optimisation produces a better individual (as established by pagmo::compare_fc()), the optimised individual will be inserted back into the population. The selection and replacement strategies can be configured via set_selection(const std::string &), set_selection(population::size_type), set_replacement(const std::string &) and set_replacement(population::size_type).
See also
The NLopt website contains a detailed description of each supported solver.
Note
This userdefined algorithm is available only if pagmo was compiled with the
PAGMO_WITH_NLOPT
option enabled (see the installation instructions).Warning
A movedfrom
pagmo::nlopt
is destructible and assignable. Any other operation will result in undefined behaviour.Public Types

using log_line_type = std::tuple<unsigned long, double, vector_double::size_type, double, bool>#
Single data line for the algorithm’s log.
A log data line is a tuple consisting of:
the number of objective function evaluations made so far,
the objective function value for the current decision vector,
the number of constraints violated by the current decision vector,
the constraints violation norm for the current decision vector,
a boolean flag signalling the feasibility of the current decision vector.

using log_type = std::vector<log_line_type>#
Log type.
The algorithm log is a collection of nlopt::log_line_type data lines, stored in chronological order during the optimisation if the verbosity of the algorithm is set to a nonzero value (see nlopt::set_verbosity()).
Public Functions

nlopt()#
Default constructor.
The default constructor initialises the pagmo::nlopt algorithm with the
"cobyla"
solver. The individual selection/replacement strategies are those specified by not_population_based::not_population_based(). Throws
unspecified – any exception thrown by pagmo::nlopt(const std::string &).

explicit nlopt(const std::string&)#
Constructor from solver name.
This constructor will initialise a pagmo::nlopt object which will use the NLopt algorithm specified by the input string
algo
. The individual selection/replacement strategies are those specified by not_population_based::not_population_based().algo
is translated to an NLopt algorithm type according to the following table:algo
stringNLopt algorithm
"cobyla"
NLOPT_LN_COBYLA
"bobyqa"
NLOPT_LN_BOBYQA
"newuoa"
NLOPT_LN_NEWUOA
"newuoa_bound"
NLOPT_LN_NEWUOA_BOUND
"praxis"
NLOPT_LN_PRAXIS
"neldermead"
NLOPT_LN_NELDERMEAD
"sbplx"
NLOPT_LN_SBPLX
"mma"
NLOPT_LD_MMA
"ccsaq"
NLOPT_LD_CCSAQ
"slsqp"
NLOPT_LD_SLSQP
"lbfgs"
NLOPT_LD_LBFGS
"tnewton_precond_restart"
NLOPT_LD_TNEWTON_PRECOND_RESTART
"tnewton_precond"
NLOPT_LD_TNEWTON_PRECOND
"tnewton_restart"
NLOPT_LD_TNEWTON_RESTART
"tnewton"
NLOPT_LD_TNEWTON
"var2"
NLOPT_LD_VAR2
"var1"
NLOPT_LD_VAR1
"auglag"
NLOPT_AUGLAG
"auglag_eq"
NLOPT_AUGLAG_EQ
See also
The NLopt website contains a detailed description of each supported solver.
 Parameters
algo – the name of the NLopt algorithm that will be used by this pagmo::nlopt object.
 Throws
std::runtime_error – if the NLopt version is not at least 2.
std::invalid_argument – if
algo
is not one of the allowed algorithm names.unspecified – any exception thrown by not_population_based::not_population_based().

nlopt(const nlopt&)#
Copy constructor.
The copy constructor will deepcopy the state of
other
. Parameters
other – the construction argument.
 Throws
unspecified – any exception thrown by copying the internal state of
other
.

population evolve(population) const#
Evolve population.
This method will select an individual from
pop
, optimise it with the NLopt algorithm specified upon construction, replace an individual inpop
with the optimised individual, and finally returnpop
. The individual selection and replacement criteria can be set via set_selection(const std::string &), set_selection(population::size_type), set_replacement(const std::string &) and set_replacement(population::size_type). The NLopt solver will run until one of the stopping criteria is satisfied, and the return status of the NLopt solver will be recorded (it can be fetched with get_last_opt_result()). Parameters
pop – the population to be optimised.
 Throws
std::invalid_argument – in the following cases:
the population’s problem is multiobjective,
the setup of the NLopt algorithm fails (e.g., if the problem is constrained but the selected NLopt solver does not support constrained optimisation),
the selected NLopt solver needs gradients but they are not provided by the population’s problem,
the components of the individual selected for optimisation contain NaNs or they are outside the problem’s bounds.
unspecified – any exception thrown by the public interface of pagmo::problem or pagmo::not_population_based.
 Returns
the optimised population.

std::string get_name() const#
Algorithm’s name.
 Returns
a humanreadable name for the algorithm.

inline void set_verbosity(unsigned n)#
Set verbosity.
This method will set the algorithm’s verbosity. If
n
is zero, no output is produced during the optimisation and no logging is performed. Ifn
is nonzero, then everyn
objective function evaluations the status of the optimisation will be both printed to screen and recorded internally. See nlopt::log_line_type and nlopt::log_type for information on the logging format. The internal log can be fetched via get_log().Example (verbosity 5):
Theobjevals: objval: violated: viol. norm: 1 47.9474 1 2.07944 i 6 17.1986 2 0.150557 i 11 17.014 0 0 16 17.014 0 0
i
at the end of some rows indicates that the decision vector is infeasible. Feasibility is checked against the problem’s tolerance.By default, the verbosity level is zero.
 Parameters
n – the desired verbosity level.

std::string get_extra_info() const#
Get extra information about the algorithm.
 Returns
a humanreadable string containing useful information about the algorithm’s properties (e.g., the stopping criteria, the selection/replacement policies, etc.).

inline const log_type &get_log() const#
Get the optimisation log.
See nlopt::log_type for a description of the optimisation log. Logging is turned on/off via set_verbosity().
 Returns
a const reference to the log.

inline std::string get_solver_name() const#
Get the name of the solver that was used to construct this pagmo::nlopt algorithm.
 Returns
the name of the NLopt solver used upon construction.

inline ::nlopt_result get_last_opt_result() const#
Get the result of the last optimisation.
 Returns
the result of the last evolve() call, or
NLOPT_SUCCESS
if no optimisations have been run yet.

inline double get_stopval() const#
Get the
stopval
stopping criterion.The
stopval
stopping criterion instructs the solver to stop when an objective value less than or equal tostopval
is found. Defaults to the C constantHUGE_VAL
(that is, this stopping criterion is disabled by default). Returns
the
stopval
stopping criterion for this pagmo::nlopt.

void set_stopval(double)#
Set the
stopval
stopping criterion. Parameters
stopval – the desired value for the
stopval
stopping criterion (see get_stopval()). Throws
std::invalid_argument – if
stopval
is NaN.

inline double get_ftol_rel() const#
Get the
ftol_rel
stopping criterion.The
ftol_rel
stopping criterion instructs the solver to stop when an optimization step (or an estimate of the optimum) changes the objective function value by less thanftol_rel
multiplied by the absolute value of the function value. Defaults to 0 (that is, this stopping criterion is disabled by default). Returns
the
ftol_rel
stopping criterion for this pagmo::nlopt.

void set_ftol_rel(double)#
Set the
ftol_rel
stopping criterion. Parameters
ftol_rel – the desired value for the
ftol_rel
stopping criterion (see get_ftol_rel()). Throws
std::invalid_argument – if
ftol_rel
is NaN.

inline double get_ftol_abs() const#
Get the
ftol_abs
stopping criterion.The
ftol_abs
stopping criterion instructs the solver to stop when an optimization step (or an estimate of the optimum) changes the function value by less thanftol_abs
. Defaults to 0 (that is, this stopping criterion is disabled by default). Returns
the
ftol_abs
stopping criterion for this pagmo::nlopt.

void set_ftol_abs(double)#
Set the
ftol_abs
stopping criterion. Parameters
ftol_abs – the desired value for the
ftol_abs
stopping criterion (see get_ftol_abs()). Throws
std::invalid_argument – if
ftol_abs
is NaN.

inline double get_xtol_rel() const#
Get the
xtol_rel
stopping criterion.The
xtol_rel
stopping criterion instructs the solver to stop when an optimization step (or an estimate of the optimum) changes every parameter by less thanxtol_rel
multiplied by the absolute value of the parameter. Defaults to 1E8. Returns
the
xtol_rel
stopping criterion for this pagmo::nlopt.

void set_xtol_rel(double)#
Set the
xtol_rel
stopping criterion. Parameters
xtol_rel – the desired value for the
xtol_rel
stopping criterion (see get_xtol_rel()). Throws
std::invalid_argument – if
xtol_rel
is NaN.

inline double get_xtol_abs() const#
Get the
xtol_abs
stopping criterion.The
xtol_abs
stopping criterion instructs the solver to stop when an optimization step (or an estimate of the optimum) changes every parameter by less thanxtol_abs
. Defaults to 0 (that is, this stopping criterion is disabled by default). Returns
the
xtol_abs
stopping criterion for this pagmo::nlopt.

void set_xtol_abs(double)#
Set the
xtol_abs
stopping criterion. Parameters
xtol_abs – the desired value for the
xtol_abs
stopping criterion (see get_xtol_abs()). Throws
std::invalid_argument – if
xtol_abs
is NaN.

inline int get_maxeval() const#
Get the
maxeval
stopping criterion.The
maxeval
stopping criterion instructs the solver to stop when the number of function evaluations exceedsmaxeval
. Defaults to 0 (that is, this stopping criterion is disabled by default). Returns
the
maxeval
stopping criterion for this pagmo::nlopt.

inline void set_maxeval(int n)#
Set the
maxeval
stopping criterion. Parameters
n – the desired value for the
maxeval
stopping criterion (see get_maxeval()).

inline int get_maxtime() const#
Get the
maxtime
stopping criterion.The
maxtime
stopping criterion instructs the solver to stop when the optimization time (in seconds) exceedsmaxtime
. Defaults to 0 (that is, this stopping criterion is disabled by default). Returns
the
maxtime
stopping criterion for this pagmo::nlopt.

inline void set_maxtime(int n)#
Set the
maxtime
stopping criterion. Parameters
n – the desired value for the
maxtime
stopping criterion (see get_maxtime()).

void set_local_optimizer(nlopt)#
Set the local optimizer.
Some NLopt algorithms rely on other NLopt algorithms as local/subsidiary optimizers. This method allows to set such local optimizer. By default, no local optimizer is specified.
Note
At the present time, only the
"auglag"
and"auglag_eq"
solvers make use of a local optimizer. Setting a local optimizer on any other solver will have no effect.Note
The objective function, bounds, and nonlinearconstraint parameters of the local optimizer are ignored (as they are provided by the parent optimizer). Conversely, the stopping criteria should be specified in the local optimizer. The verbosity of the local optimizer is also forcibly set to zero during the optimisation.
 Parameters
n – the local optimizer that will be used by this pagmo::nlopt algorithm.

inline const nlopt *get_local_optimizer() const#
Get the local optimizer.
This method returns a raw const pointer to the local optimizer, if it has been set via set_local_optimizer(). Otherwise,
nullptr
will be returned.Note
The returned value is a raw nonowning pointer: the lifetime of the pointee is tied to the lifetime of
this
, anddelete
must never be called on the pointer. Returns
a const pointer to the local optimizer.

inline nlopt *get_local_optimizer()#
Get the local optimizer.
This method returns a raw pointer to the local optimizer, if it has been set via set_local_optimizer(). Otherwise,
nullptr
will be returned.Note
The returned value is a raw nonowning pointer: the lifetime of the pointee is tied to the lifetime of
this
, anddelete
must never be called on the pointer.Note
The ability to extract a mutable pointer is provided only in order to allow to call nonconst methods on the local optimizer. Assigning a new local optimizer via this pointer is undefined behaviour.
 Returns
a pointer to the local optimizer.

void unset_local_optimizer()#
Unset the local optimizer.
After a call to this method, get_local_optimizer() and get_local_optimizer() const will return
nullptr
.

void set_random_sr_seed(unsigned)#
Set the seed for the
"random"
selection/replacement policies. Parameters
seed – the value that will be used to seed the random number generator used by the
"random"
selection/replacement policies.

void set_selection(const std::string&)#
Set the individual selection policy.
This method will set the policy that is used to select the individual that will be optimised when calling the
evolve()
method of the algorithm.The input string must be one of
"best"
,"worst"
and"random"
:"best"
will select the best individual in the population,"worst"
will select the worst individual in the population,"random"
will randomly choose one individual in the population.
set_random_sr_seed() can be used to seed the random number generator used by the
"random"
policy.Instead of a selection policy, a specific individual in the population can be selected via set_selection(population::size_type).
 Parameters
select – the selection policy.
 Throws
std::invalid_argument – if
select
is not one of"best"
,"worst"
or"random"
.

inline void set_selection(population::size_type n)#
Set the individual selection index.
This method will set the index of the individual that is selected for optimisation in the
evolve()
method of the algorithm. Parameters
n – the index in the population of the individual to be selected for optimisation.

boost::any get_selection() const#
Get the individual selection policy or index.
This method will return a
boost::any
containing either the individual selection policy (as anstd::string
) or the individual selection index (as a population::size_type). The selection policy or index is set via set_selection(const std::string &) and set_selection(population::size_type). Returns
the individual selection policy or index.

void set_replacement(const std::string&)#
Set the individual replacement policy.
This method will set the policy that is used in the
evolve()
method of the algorithm to select the individual that will be replaced by the optimised individual.The input string must be one of
"best"
,"worst"
and"random"
:"best"
will select the best individual in the population,"worst"
will select the worst individual in the population,"random"
will randomly choose one individual in the population.
set_random_sr_seed() can be used to seed the random number generator used by the
"random"
policy.Instead of a replacement policy, a specific individual in the population can be selected via set_replacement(population::size_type).
 Parameters
replace – the replacement policy.
 Throws
std::invalid_argument – if
replace
is not one of"best"
,"worst"
or"random"
.

inline void set_replacement(population::size_type n)#
Set the individual replacement index.
This method will set the index of the individual that is replaced after the optimisation in the
evolve()
method of the algorithm. Parameters
n – the index in the population of the individual to be replaced after the optimisation.

boost::any get_replacement() const#
Get the individual replacement policy or index.
This method will return a
boost::any
containing either the individual replacement policy (as anstd::string
) or the individual replacement index (as a population::size_type). The replacement policy or index is set via set_replacement(const std::string &) and set_replacement(population::size_type). Returns
the individual replacement policy or index.