Source code for geomstats.numerics.optimizers

"""Optimizers implementations."""

import logging

import scipy

import geomstats.backend as gs
from geomstats.exceptions import AutodiffNotImplementedError
from geomstats.numerics._common import result_to_backend_type

[docs] class ScipyMinimize: """Wrapper for scipy.optimize.minimize. Only `jac` differs from scipy: if `autodiff`, then `gs.autodiff.value_and_grad` is used to compute the jacobian. """ def __init__( self, method="L-BFGS-B", jac=None, hess=None, bounds=None, constraints=(), tol=None, callback=None, options=None, save_result=False, ): if jac == "autodiff" and gs.__name__.endswith("numpy"): raise AutodiffNotImplementedError( "Minimization with 'autodiff' requires automatic differentiation." "Change backend via the command " "export GEOMSTATS_BACKEND=pytorch in a terminal" ) self.method = method self.jac = jac self.hess = hess self.bounds = bounds self.constraints = constraints self.tol = tol self.callback = callback self.options = options self.save_result = save_result self.result_ = None def _handle_jac(self, fun, fun_jac): if fun_jac is not None: return fun, fun_jac jac = self.jac if self.jac == "autodiff": jac = True def fun_(x): value, grad = gs.autodiff.value_and_grad(fun)(gs.from_numpy(x)) return value, grad else: def fun_(x): return fun(gs.from_numpy(x)) return fun_, jac def _handle_hess(self, fun_hess): if fun_hess is not None: return fun_hess return self.hess
[docs] def minimize(self, fun, x0, fun_jac=None, fun_hess=None, hessp=None): """Minimize objective function. Parameters ---------- fun : callable The objective function to be minimized. x0 : array-like Initial guess. fun_jac : callable If not None, jac is ignored. fun_hess : callable If not None, hess is ignored. hessp : callable """ fun_, jac = self._handle_jac(fun, fun_jac) hess = self._handle_hess(fun_hess) result = scipy.optimize.minimize( fun_, x0, method=self.method, jac=jac, hess=hess, hessp=hessp, bounds=self.bounds, tol=self.tol, constraints=self.constraints, callback=self.callback, options=self.options, ) result = result_to_backend_type(result) if result.status > 0: logging.warning(result.message) if self.save_result: self.result_ = result return result