def sgd(g, maxiter=300, step_rule=None, verbose=False, make_log=False, heavy_ball=False, parallel=0):
g = deepcopy(g)
process_pool = utils.MyPool(parallel) if parallel else None
best_primal = 2 ** 32
best_dual = -2 ** 32
update, energy, primal_energy = computeSubgradient(g, process_pool=process_pool)
optimal_objective, optimal_primal = lp.solveLPonLocalPolytope(g)
current_dual_vector = {}
scope = {}
if step_rule[0] == 'step_adaptive':
scope['delta'] = (primal_energy - energy) / 2
scope['energy_rec'] = energy
scope['without_imp'] = 0
scope['max_without_imp'] = 10
optimal_solution = g.optimal_parameters if hasattr(g, 'optimal_parameters') else None
i = 0
log = []
update = {}
while i < maxiter:
update_new, energy, primal_energy = computeSubgradient(g, process_pool=process_pool)
if heavy_ball and i > 0:
beta = max(0.0, (-1.5 * utils.dictDot(update, update_new)) / (utils.dictDot(update, update)))
else:
beta = 0.0
update = utils.dictSum(update_new, utils.dictMult(update, beta))
best_dual = max(best_dual, energy)
best_primal = min(best_primal, primal_energy)
sn = np.linalg.norm(update.values())
if sn < 1.0e-9:
break
def step_constant(r0=0.01):
return r0
def step_array(a):
return a[i]
def step_power(r0=1.0, alpha=0.5):
return r0 / ((1 + i ** alpha))
def step_power_norm(r0=1.0, alpha=0.5):
return r0 / ((1 + i ** alpha) * sn)
def step_god():
return utils.dictDot(update, utils.dictDiff(optimal_solution, current_dual_vector)) / sn ** 2
def step_godobjective():
return (optimal_objective - energy) / sn ** 2
def step_supergod():
step, projection = lp.optimalStepDD(g, optimal_primal, current_dual_vector, update)
return step
def step_adaptive(gamma=1.0, **kwarg):
update = 0
if energy > scope['energy_rec'] + scope['delta'] / 5:
update = 1
elif scope['without_imp'] > scope['max_without_imp']:
scope['delta'] = scope['delta'] * 0.5
update = 1
scope['without_imp'] += 1
if update:
scope['energy_rec'] = best_dual
scope['without_imp'] = 0
approx = scope['energy_rec'] + scope['delta']
return gamma * ((approx - energy) / sn ** 2)
step = eval(step_rule[0])(**step_rule[1])
if verbose:
print(i, "%.4f" % best_primal, "%.4f" % best_dual, "%.4f" % energy, "%.4f" % step)
if make_log:
distance = np.linalg.norm(utils.dictDiff(optimal_solution, current_dual_vector).values())
gn = np.sum([tree.n_factors for tree in g.tree_decomposition])
theoretical_imp = (optimal_objective - energy) ** 2 / gn
log_line = [best_primal, best_dual, energy, sn, distance, step, theoretical_imp, optimal_objective]
log.append(log_line)
updateDDParams(g, update, step)
current_dual_vector = utils.dictSum(current_dual_vector, utils.dictMult(update, step))
i += 1
if make_log:
return current_dual_vector, np.array(log)
return current_dual_vector
def step_god():
return utils.dictDot(update, utils.dictDiff(optimal_solution, current_dual_vector)) / sn ** 2
