Source code for pymanopt.solvers.solver

import abc
import time


[docs]class Solver(metaclass=abc.ABCMeta): ''' Abstract base class setting out template for solver classes. ''' def __init__(self, maxtime=1000, maxiter=1000, mingradnorm=1e-6, minstepsize=1e-10, maxcostevals=5000, logverbosity=0): """ Variable attributes (defaults in brackets): - maxtime (1000) Max time (in seconds) to run. - maxiter (1000) Max number of iterations to run. - mingradnorm (1e-6) Terminate if the norm of the gradient is below this. - minstepsize (1e-10) Terminate if linesearch returns a vector whose norm is below this. - maxcostevals (5000) Maximum number of allowed cost evaluations - logverbosity (0) Level of information logged by the solver while it operates, 0 is silent, 2 ist most information. """ self._maxtime = maxtime self._maxiter = maxiter self._mingradnorm = mingradnorm self._minstepsize = minstepsize self._maxcostevals = maxcostevals self._logverbosity = logverbosity self._optlog = None def __str__(self): return type(self).__name__
[docs] @abc.abstractmethod def solve(self, problem, x=None): ''' Solve the given :py:class:`pymanopt.core.problem.Problem` (starting from a random initial guess if the optional argument x is not provided). ''' pass
def _check_stopping_criterion(self, time0, iter=-1, gradnorm=float('inf'), stepsize=float('inf'), costevals=-1): reason = None if time.time() >= time0 + self._maxtime: reason = ("Terminated - max time reached after %d iterations." % iter) elif iter >= self._maxiter: reason = ("Terminated - max iterations reached after " "%.2f seconds." % (time.time() - time0)) elif gradnorm < self._mingradnorm: reason = ("Terminated - min grad norm reached after %d " "iterations, %.2f seconds." % ( iter, (time.time() - time0))) elif stepsize < self._minstepsize: reason = ("Terminated - min stepsize reached after %d iterations, " "%.2f seconds." % (iter, (time.time() - time0))) elif costevals >= self._maxcostevals: reason = ("Terminated - max cost evals reached after " "%.2f seconds." % (time.time() - time0)) return reason def _start_optlog(self, solverparams=None, extraiterfields=None): if self._logverbosity <= 0: self._optlog = None else: self._optlog = {'solver': str(self), 'stoppingcriteria': {'maxtime': self._maxtime, 'maxiter': self._maxiter, 'mingradnorm': self._mingradnorm, 'minstepsize': self._minstepsize, 'maxcostevals': self._maxcostevals}, 'solverparams': solverparams } if self._logverbosity >= 2: if extraiterfields: self._optlog['iterations'] = {'iteration': [], 'time': [], 'x': [], 'f(x)': []} for field in extraiterfields: self._optlog['iterations'][field] = [] def _append_optlog(self, iteration, x, fx, **kwargs): # In case not every iteration is being logged self._optlog['iterations']['iteration'].append(iteration) self._optlog['iterations']['time'].append(time.time()) self._optlog['iterations']['x'].append(x) self._optlog['iterations']['f(x)'].append(fx) for key in kwargs: self._optlog['iterations'][key].append(kwargs[key]) def _stop_optlog(self, x, objective, stop_reason, time0, stepsize=float('inf'), gradnorm=float('inf'), iter=-1, costevals=-1): self._optlog['stoppingreason'] = stop_reason self._optlog['final_values'] = {'x': x, 'f(x)': objective, 'time': time.time() - time0} if stepsize is not float('inf'): self._optlog['final_values']['stepsize'] = stepsize if gradnorm is not float('inf'): self._optlog['final_values']['gradnorm'] = gradnorm if iter is not -1: self._optlog['final_values']['iterations'] = iter if costevals is not -1: self._optlog['final_values']['costevals'] = costevals