optunity.api module¶
A collection of top-level API functions for Optunity.
Main functions in this module:
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 if
- solver_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 if solver_name is not registered.
- optunity.api.maximize(f, num_evals=50, solver_name=None, pmap=<built-in 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.minimize(f, num_evals=50, solver_name=None, pmap=<built-in 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.optimize(solver, func, maximize=True, max_evals=0, pmap=<built-in function map>)[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
