Source code for pymanopt.solvers.steepest_descent

import time
from copy import deepcopy

from pymanopt.solvers.linesearch import LineSearchBackTracking
from pymanopt.solvers.solver import Solver

[docs]class SteepestDescent(Solver): """ Steepest descent (gradient descent) algorithm based on steepestdescent.m from the manopt MATLAB package. """ def __init__(self, linesearch=None, *args, **kwargs): super().__init__(*args, **kwargs) if linesearch is None: self._linesearch = LineSearchBackTracking() else: self._linesearch = linesearch self.linesearch = None # Function to solve optimisation problem using steepest descent.
[docs] def solve(self, problem, x=None, reuselinesearch=False): """ Perform optimization using gradient descent with linesearch. This method first computes the gradient (derivative) of obj w.r.t. arg, and then optimizes by moving in the direction of steepest descent (which is the opposite direction to the gradient). Arguments: - problem Pymanopt problem setup using the Problem class, this must have a .manifold attribute specifying the manifold to optimize over, as well as a cost and enough information to compute the gradient of that cost. - x=None Optional parameter. Starting point on the manifold. If none then a starting point will be randomly generated. - reuselinesearch=False Whether to reuse the previous linesearch object. Allows to use information from a previous solve run. Returns: - x Local minimum of obj, or if algorithm terminated before convergence x will be the point at which it terminated. """ man = problem.manifold verbosity = problem.verbosity objective = problem.cost gradient = problem.grad if not reuselinesearch or self.linesearch is None: self.linesearch = deepcopy(self._linesearch) linesearch = self.linesearch # If no starting point is specified, generate one at random. if x is None: x = man.rand() # Initialize iteration counter and timer iter = 0 time0 = time.time() if verbosity >= 2: print(" iter\t\t cost val\t grad. norm") self._start_optlog(extraiterfields=['gradnorm'], solverparams={'linesearcher': linesearch}) while True: # Calculate new cost, grad and gradnorm cost = objective(x) grad = gradient(x) gradnorm = man.norm(x, grad) iter = iter + 1 if verbosity >= 2: print("%5d\t%+.16e\t%.8e" % (iter, cost, gradnorm)) if self._logverbosity >= 2: self._append_optlog(iter, x, cost, gradnorm=gradnorm) # Descent direction is minus the gradient desc_dir = -grad # Perform line-search stepsize, x =, man, x, desc_dir, cost, -gradnorm**2) stop_reason = self._check_stopping_criterion( time0, stepsize=stepsize, gradnorm=gradnorm, iter=iter) if stop_reason: if verbosity >= 1: print(stop_reason) print('') break if self._logverbosity <= 0: return x else: self._stop_optlog(x, objective(x), stop_reason, time0, stepsize=stepsize, gradnorm=gradnorm, iter=iter) return x, self._optlog