optunity.api module¶
A collection of toplevel API functions for Optunity.
Main functions in this module:
make_solver()
suggest_solver()
manual()
maximize()
maximize_structured()
minimize()
minimize_structured()
optimize()
We recommend using these functions rather than equivalents found in other places,
e.g. optunity.solvers
.
Module author: Marc Claesen

optunity.api.
available_solvers
()[source]¶ Returns a list of all available solvers.
These can be used in
optunity.make_solver()
.

optunity.api.
make_solver
(solver_name, *args, **kwargs)[source]¶ Creates a Solver from given parameters.
Parameters:  solver_name (string) – the solver to instantiate
 args – positional arguments to solver constructor.
 kwargs – keyword arguments to solver constructor.
Use
optunity.manual()
to get a list of registered solvers. For constructor arguments per solver, please refer to Solver overview.Raises
KeyError
ifsolver_name
is not registered*args
and**kwargs
are invalid to instantiate the solver.

optunity.api.
manual
(solver_name=None)[source]¶ Prints the manual of requested solver.
Parameters: solver_name – (optional) name of the solver to request a manual from. If none is specified, a general manual is printed. Raises
KeyError
ifsolver_name
is not registered.

optunity.api.
maximize
(f, num_evals=50, solver_name=None, pmap=<builtin function map>, **kwargs)[source]¶ Basic function maximization routine. Maximizes
f
within the given box constraints.Parameters:  f – the function to be maximized
 num_evals – number of permitted function evaluations
 solver_name (string) – name of the solver to use (optional)
 pmap (callable) – the map function to use
 kwargs – box constraints, a dict of the following form
{'parameter_name': [lower_bound, upper_bound], ...}
Returns: retrieved maximum, extra information and solver info
This function will implicitly choose an appropriate solver and its initialization based on
num_evals
and the box constraints.

optunity.api.
maximize_structured
(f, search_space, num_evals=50, pmap=<builtin function map>)[source]¶ Basic function maximization routine. Maximizes
f
within the given box constraints.Parameters:  f – the function to be maximized
 search_space – the search space (see Structured search spaces for details)
 num_evals – number of permitted function evaluations
 pmap (callable) – the map function to use
Returns: retrieved maximum, extra information and solver info
This function will implicitly choose an appropriate solver and its initialization based on
num_evals
and the box constraints.

optunity.api.
minimize
(f, num_evals=50, solver_name=None, pmap=<builtin function map>, **kwargs)[source]¶ Basic function minimization routine. Minimizes
f
within the given box constraints.Parameters:  f – the function to be minimized
 num_evals – number of permitted function evaluations
 solver_name (string) – name of the solver to use (optional)
 pmap (callable) – the map function to use
 kwargs – box constraints, a dict of the following form
{'parameter_name': [lower_bound, upper_bound], ...}
Returns: retrieved minimum, extra information and solver info
This function will implicitly choose an appropriate solver and its initialization based on
num_evals
and the box constraints.

optunity.api.
minimize_structured
(f, search_space, num_evals=50, pmap=<builtin function map>)[source]¶ Basic function minimization routine. Minimizes
f
within the given box constraints.Parameters:  f – the function to be maximized
 search_space – the search space (see Structured search spaces for details)
 num_evals – number of permitted function evaluations
 pmap (callable) – the map function to use
Returns: retrieved maximum, extra information and solver info
This function will implicitly choose an appropriate solver and its initialization based on
num_evals
and the box constraints.

optunity.api.
optimize
(solver, func, maximize=True, max_evals=0, pmap=<builtin function map>, decoder=None)[source]¶ Optimizes func with given solver.
Parameters:  solver – the solver to be used, for instance a result from
optunity.make_solver()
 func (callable) – the objective function
 maximize (bool) – maximize or minimize?
 max_evals (int) – maximum number of permitted function evaluations
 pmap (function) – the map() function to use, to vectorize use
optunity.pmap()
Returns the solution and a
namedtuple
with further details.Result details includes the following:
 optimum
 optimal function value f(solution)
 stats
 statistics about the solving process
 call_log
 the call log
 report
 solver report, can be None
Statistics gathered while solving a problem:
 num_evals
 number of function evaluations
 time
 wall clock time needed to solve
 solver – the solver to be used, for instance a result from