def test_1(self):
'''
The simplest case that the black box has only two inputs and there is only one black block involved
'''
def blackbox(a,b):
return sin(a-b)
m = self.m
bb = ExternalFunction(blackbox)
m.eflist = [bb]
m.c1 = Constraint(expr=m.x[0] * m.z[0]**2 + bb(m.x[0],m.x[1]) == 2*sqrt(2.0))
pI = PyomoInterface(m, [bb], ConfigBlock())
self.assertEqual(pI.lx,2)
self.assertEqual(pI.ly,1)
self.assertEqual(pI.lz,3)
self.assertEqual(len(list(identify_variables(m.c1.body))),3)
self.assertEqual(len(list(identify_variables(m.c2.body))),2)
def test_2(self):
'''
The simplest case that the black box has only one inputs and there is only a formula
'''
def blackbox(a):
return sin(a)
m = self.m
bb = ExternalFunction(blackbox)
m.eflist = [bb]
m.c1 = Constraint(expr=m.x[0] * m.z[0]**2 + bb(m.x[0]-m.x[1]) == 2*sqrt(2.0))
pI = PyomoInterface(m, [bb], ConfigBlock())
self.assertEqual(pI.lx,1)
self.assertEqual(pI.ly,1)
self.assertEqual(pI.lz,5)
self.assertEqual(len(list(identify_variables(m.c1.body))),3)
self.assertEqual(len(list(identify_variables(m.c2.body))),2)
self.assertEqual(len(m.tR.conset),1)
self.assertEqual(len(list(identify_variables(m.tR.conset[1].body))),3)
def TRF(m, eflist, config):
"""The main function of the Trust Region Filter algorithm
m is a PyomoModel containing ExternalFunction() objects Model
requirements: m is a nonlinear program, with exactly one active
objective function.
eflist is a list of ExternalFunction objects that should be
treated with the trust region
config is the persistent set of variables defined
in the ConfigBlock class object
Return:
model is solved, variables are at optimal solution or
other exit condition. model is left in reformulated form, with
some new variables introduced in a block named "tR" TODO: reverse
the transformation.
"""
logger = Logger()
filteR = Filter()
problem = PyomoInterface(m, eflist)
x, y, z = problem.getInitialValue()
iteration = -1
romParam, yr = problem.buildROM(x, config.sample_radius)
#y = yr
rebuildROM = False
xk, yk, zk = cloneXYZ(x, y, z)
chik = 1e8
thetak = norm(yr - yk, 1)
objk = problem.evaluateObj(x, y, z)
while True:
if iteration >= 0:
logger.printIteration(iteration)
#print(xk)
# increment iteration counter
iteration = iteration + 1
if iteration > config.max_it:
print("EXIT: Maxmium iterations\n")
break
###### Why is this here ###########
if iteration == 1:
config.sample_region = False
################################
# Keep Sample Region within Trust Region
if config.trust_radius < config.sample_radius:
config.sample_radius = max(
config.sample_radius_adjust * config.trust_radius,
config.delta_min)
rebuildROM = True
#Generate a RM r_k (x) that is kappa-fully linear on sigma k
if (rebuildROM):
#TODO: Ask Jonathan what variable 1e-3 should be
if config.trust_radius < 1e-3:
problem.romtype = ROMType.linear
else:
problem.romtype = config.reduced_model_type
romParam, yr = problem.buildROM(x, config.sample_radius)
#print(romParam)
#print(config.sample_radius)
# Criticality Check
if iteration > 0:
flag, chik = problem.criticalityCheck(x, y, z, romParam)
if (not flag):
raise Exception("Criticality Check fails!\n")
# Save the iteration information to the logger
logger.newIter(iteration, xk, yk, zk, thetak, objk, chik,
config.print_variables)
# Check for Termination
if (thetak < config.ep_i and chik < config.ep_chi
and config.sample_radius < config.ep_delta):
print("EXIT: OPTIMAL SOLUTION FOUND")
break
# If trust region very small and no progress is being made,
# terminate. The following condition must hold for two
# consecutive iterations.
if (config.trust_radius <= config.delta_min and thetak < config.ep_i):
if subopt_flag:
print("EXIT: FEASIBLE SOLUTION FOUND ")
break
else:
subopt_flag = True
else:
# This condition holds for iteration 0, which will declare
# the boolean subopt_flag
subopt_flag = False
# New criticality phase
if not config.sample_region:
config.sample_radius = config.trust_radius / 2.0
if config.sample_radius > chik * config.criticality_check:
config.sample_radius = config.sample_radius / 10.0
config.trust_radius = config.sample_radius * 2
else:
config.sample_radius = max(
min(config.sample_radius, chik * config.criticality_check),
config.delta_min)
logger.setCurIter(trustRadius=config.trust_radius,
sampleRadius=config.sample_radius)
# Compatibility Check (Definition 2)
# radius=max(kappa_delta*config.trust_radius*min(1,kappa_mu*config.trust_radius**mu),
# delta_min)
radius = max(
config.kappa_delta * config.trust_radius *
min(1, config.kappa_mu * pow(config.trust_radius, config.mu)),
config.delta_min)
try:
flag, obj = problem.compatibilityCheck(
x, y, z, xk, yk, zk, romParam, radius,
config.compatibility_penalty)
except:
print("Compatibility check failed, unknown error")
raise
if not flag:
raise Exception("Compatibility check fails!\n")
theNorm = norm(x - xk, 2)**2 + norm(z - zk, 2)**2
if (obj - config.compatibility_penalty * theNorm >
config.ep_compatibility):
# Restoration stepNorm
yr = problem.evaluateDx(x)
theta = norm(yr - y, 1)
logger.iterlog.restoration = True
fe = FilterElement(objk - config.gamma_f * thetak,
(1 - config.gamma_theta) * thetak)
filteR.addToFilter(fe)
rhok = 1 - ((theta - config.ep_i) / max(thetak, config.ep_i))
if rhok < config.eta1:
config.trust_radius = max(config.gamma_c * config.trust_radius,
config.delta_min)
elif rhok >= config.eta2:
config.trust_radius = min(config.gamma_e * config.trust_radius,
config.radius_max)
obj = problem.evaluateObj(x, y, z)
stepNorm = norm(packXYZ(x - xk, y - yk, z - zk), inf)
logger.setCurIter(stepNorm=stepNorm)
else:
# Solve TRSP_k
flag, obj = problem.TRSPk(x, y, z, xk, yk, zk, romParam,
config.trust_radius)
if not flag:
raise Exception("TRSPk fails!\n")
# Filter
yr = problem.evaluateDx(x)
stepNorm = norm(packXYZ(x - xk, y - yk, z - zk), inf)
logger.setCurIter(stepNorm=stepNorm)
theta = norm(yr - y, 1)
fe = FilterElement(obj, theta)
if not filteR.checkAcceptable(fe,
config.theta_max) and iteration > 0:
logger.iterlog.rejected = True
config.trust_radius = max(config.gamma_c * stepNorm,
config.delta_min)
rebuildROM = False
x, y, z = cloneXYZ(xk, yk, zk)
continue
# Switching Condition and Trust Region update
if (((objk - obj) >=
config.kappa_theta * pow(thetak, config.gamma_s))
and (thetak < config.theta_min)):
logger.iterlog.fStep = True
config.trust_radius = min(
max(config.gamma_e * stepNorm, config.trust_radius),
config.radius_max)
else:
logger.iterlog.thetaStep = True
fe = FilterElement(obj - config.gamma_f * theta,
(1 - config.gamma_theta) * theta)
filteR.addToFilter(fe)
# Calculate rho for theta step trust region update
rhok = 1 - ((theta - config.ep_i) / max(thetak, config.ep_i))
if rhok < config.eta1:
config.trust_radius = max(config.gamma_c * stepNorm,
config.delta_min)
elif rhok >= config.eta2:
config.trust_radius = min(
max(config.gamma_e * stepNorm, config.trust_radius),
config.radius_max)
# Accept step
rebuildROM = True
xk, yk, zk = cloneXYZ(x, y, z)
thetak = theta
objk = obj
logger.printVectors()
def TRF(m, eflist):
"""
The main function of the Trust Region Filter algorithm
m is a PyomoModel containing ExternalFunction() objects
Model requirements: m is a nonlinear program, with exactly one active objective function.
eflist is a list of ExternalFunction objects that should be treated with the
trust region
Return:
model is solved, variables are at optimal solution or other exit condition.
model is left in reformulated form, with some new variables introduced
in a block named "tR" TODO: reverse the transformation.
"""
logger = Logger()
filteR = Filter()
problem = PyomoInterface(m, eflist)
x, y, z = problem.getInitialValue()
trustRadius = TRUST_RADIUS
sampleRadius = SAMPLE_RADIUS
sampleregion_yn = SAMPLEREGION_YN
iteration = -1
romParam, yr = problem.buildROM(x, sampleRadius)
#y = yr
rebuildROM = False
xk, yk, zk = cloneXYZ(x, y, z)
chik = 1e8
thetak = norm(yr - yk, 1)
objk = problem.evaluateObj(x, y, z)
while True:
if (iteration >= 0):
logger.printIteration(iteration)
#print(xk)
# increment iteration counter
iteration = iteration + 1
if (iteration > MAXIT):
print("EXIT: Maxmium iterations\n")
break
###### Why is this here ###########
if iteration == 1:
sampleregion_yn = False
################################
# Keep Sample Region within Trust Region
if trustRadius < sampleRadius:
sampleRadius = max(SR_ADJUST * trustRadius, DELTMIN)
rebuildROM = True
#Generate a RM r_k (x) that is κ-fully linear on sigma k
if (rebuildROM):
if trustRadius < 1e-3:
problem.romtype = ROMType.linear
else:
problem.romtype = DEFAULT_ROMTYPE
romParam, yr = problem.buildROM(x, sampleRadius)
#print(romParam)
#print(sampleRadius)
# Criticality Check
if iteration > 0:
flag, chik = problem.criticalityCheck(x, y, z, romParam)
if (not flag):
raise Exception("Criticality Check fails!\n")
# Save the iteration information to the logger
logger.newIter(iteration, xk, yk, zk, thetak, objk, chik)
# Check for Termination
if thetak < EP_I and chik < EP_CHI and sampleRadius < EP_DELT:
print("EXIT: OPTIMAL SOLUTION FOUND")
break
# If trust region very small and no progress is being made, terminate
# The following condition must hold for two consecutive iterations.
if trustRadius <= DELTMIN and thetak < EP_I:
if subopt_flag:
print("EXIT: FEASIBLE SOLUTION FOUND ")
break
else:
subopt_flag = True
else:
# This condition holds for iteration 0, which will declare the boolean subopt_flag
subopt_flag = False
# New criticality phase
if not sampleregion_yn:
sampleRadius = trustRadius / 2.0
if sampleRadius > chik * CRITICALITY_CHECK:
sampleRadius = sampleRadius / 10.0
trustRadius = sampleRadius * 2
else:
sampleRadius = max(min(sampleRadius, chik * CRITICALITY_CHECK),
DELTMIN)
logger.setCurIter(trustRadius=trustRadius, sampleRadius=sampleRadius)
# Compatibility Check
radius = max(KAPPA_DELTA * trustRadius * \
min(1, KAPPA_MU * pow(trustRadius, MU)),DELTMIN)
try:
flag, obj = problem.compatibilityCheck(x, y, z, xk, yk, zk,
romParam, radius,
COMPAT_PENALTY)
except:
print("Compatibility check failed, unknown error")
raise
if not flag:
raise Exception("Compatibility check fails!\n")
if (obj - COMPAT_PENALTY *
(norm(x - xk, 2)**2 + norm(z - zk, 2)**2) > EP_COMPAT):
# Restoration stepNorm
yr = problem.evaluateDx(x)
theta = norm(yr - y, 1)
logger.iterlog.restoration = True
fe = FilterElement(objk - GAMMA_F * thetak,
(1 - GAMMA_THETA) * thetak)
filteR.addToFilter(fe)
rhok = 1 - (theta - EP_I) / max(thetak, EP_I)
if (rhok < ETA1):
trustRadius = max(GAMMA_C * trustRadius, DELTMIN)
elif (rhok >= ETA2):
trustRadius = min(GAMMA_E * trustRadius, RADIUS_MAX)
obj = problem.evaluateObj(x, y, z)
stepNorm = norm(packXYZ(x - xk, y - yk, z - zk), inf)
logger.setCurIter(stepNorm=stepNorm)
else:
# Solve TRSP_k
flag, obj = problem.TRSPk(x, y, z, xk, yk, zk, romParam,
trustRadius)
if not flag:
raise Exception("TRSPk fails!\n")
# Filter
yr = problem.evaluateDx(x)
stepNorm = norm(packXYZ(x - xk, y - yk, z - zk), inf)
logger.setCurIter(stepNorm=stepNorm)
theta = norm(yr - y, 1)
fe = FilterElement(obj, theta)
if not filteR.checkAcceptable(fe) and iteration > 0:
logger.iterlog.rejected = True
trustRadius = max(GAMMA_C * stepNorm, DELTMIN)
rebuildROM = False
x, y, z = cloneXYZ(xk, yk, zk)
continue
# Switching Condition and Trust Region update
if ((objk - obj) >= KAPPA_THETA * pow(thetak, GAMMA_S)
and thetak < THETA_MIN):
logger.iterlog.fStep = True
trustRadius = min(max(GAMMA_E * stepNorm, trustRadius),
RADIUS_MAX)
else:
logger.iterlog.thetaStep = True
fe = FilterElement(obj - GAMMA_F * theta,
(1 - GAMMA_THETA) * theta)
filteR.addToFilter(fe)
# Calculate rho for theta step trust region update
rhok = 1 - (theta - EP_I) / max(thetak, EP_I)
if (rhok < ETA1):
trustRadius = max(GAMMA_C * stepNorm, DELTMIN)
elif (rhok >= ETA2):
trustRadius = min(max(GAMMA_E * stepNorm, trustRadius),
RADIUS_MAX)
# Accept step
rebuildROM = True
xk, yk, zk = cloneXYZ(x, y, z)
thetak = theta
objk = obj
logger.printVectors()