def test_gutmann_ex8_1_4_log(self):
"""Check solution of ex8_1_4 with Gutmann, log scaling, infstep.
Sampling-based global search.
"""
bb = ti.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving branin with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(algorithm='Gutmann',
rbf='multiquadric',
global_search_method='sampling',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
function_scaling='log',
do_infstep=True,
rand_seed=seed)
alg = ra.OptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve branin with Gutmann\'s algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_msrsm_parallel_st_miqp3_no_local_search(self):
"""Check solution of st_miqp3 with MSRSM and no local search.
Solver solution of global search problems."""
bb = ti.TestBlackBox('st_miqp3')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving st_miqp3 with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(rbf='cubic',
global_search_method='genetic',
algorithm='MSRSM',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
num_cpus=4,
local_search_box_scaling=10000,
rand_seed=seed)
alg = ra.OptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve st_miqp3 with MSRSM algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_state_reload(self):
"""Check solution of ex8_1_4 after state save/reload."""
bb = ti.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
handle, filename = tempfile.mkstemp()
for seed in self.rand_seeds:
print()
print('Solving ex8_1_4 with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(algorithm='MSRSM',
rbf='linear',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
rand_seed=seed)
alg = ra.OptAlgorithm(settings, bb)
res = alg.optimize(5)
alg.save_to_file(filename)
alg_reload = ra.OptAlgorithm.load_from_file(filename)
res = alg_reload.optimize()
msg = 'Could not solve ex8_1_4 after reload'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
os.close(handle)
os.remove(filename)
def test_time_limit(self):
"""Verify that time limits are satisfied."""
bb = ti.TestBlackBox('hartman6')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving hartman6 with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(algorithm='MSRSM',
global_search_method='solver',
target_objval=optimum,
eps_opt=0.0,
max_clock_time=2.0,
rand_seed=seed)
start_time = time.time()
alg = ra.OptAlgorithm(settings, bb)
res = alg.optimize()
tot_time = time.time() - start_time
msg = 'Time limit exceeded with MSRSM algorithm'
self.assertLessEqual(tot_time, 5.0, msg=msg)
def test_msrsm_prob03(self):
"""Check solution of prob03 with the MSRSM algorithm, genetic."""
bb = ti.TestBlackBox('prob03')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving prob03 with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(algorithm='MSRSM',
global_search_method='genetic',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
rand_seed=seed)
alg = ra.OptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve prob03 with MSRSM algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_gutmann_ex8_1_4(self):
"""Check solution of ex8_1_4 with Gutmann's method, genetic."""
bb = ti.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving ex8_1_4 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='Gutmann',
global_search_method='genetic',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
rand_seed=seed)
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve ex8_1_4 with Gutmann\'s algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_msrsm_parallel_ex8_1_4(self):
"""Check solution of ex8_1_4 with the MSRSM algorithm, sampling."""
bb = ti.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving ex8_1_4 with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(algorithm='MSRSM',
global_search_method='sampling',
rbf='linear',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
num_cpus=2,
rand_seed=seed)
alg = ra.OptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve ex8_1_4 with MSRSM algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_gutmann_parallel_prob03(self):
"""Check solution of prob03 with Gutmann's method, sampling."""
bb = ti.TestBlackBox('prob03')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving prob03 with random seed ' + '{:d}'.format(seed))
settings = RbfSettings(algorithm='Gutmann',
global_search_method='sampling',
rbf='cubic',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
num_cpus=2,
rand_seed=seed)
alg = ra.OptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve prob03 with Gutmann\'s algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
