opt.optimize()
# Get the optimized point
x, z, zw, zl, zu = opt.getOptimizedPoint()
# Write the solution out to a file
if i % args.output_freq == 0:
# Compute the iteration counter
itr = ite * args.max_opt_iters + i
# Get the vector and convert it
vec = problem.convertPVecToVec(x)
for k in range(vars_per_node):
f = 'levelset05_var%d_binary%04d.bstl' % (k, itr)
filename = os.path.join(args.prefix, f)
TMR.writeSTLToBin(filename, filtr, vec, offset=k)
# Update the trust region method
infeas, l1, linfty = tr.update(x, z, zw)
# Check for convergence using relatively strict tolerances
if infeas < 1e-4 and l1 < 0.01:
break
# Set the old values of the variables
old_x, z, zw, zl, zu = opt.getOptimizedPoint()
elif use_paropt:
# Create the ParOpt problem
opt = ParOpt.pyParOpt(problem, args.max_lbfgs, ParOpt.BFGS)
# Set parameters
opt.setMaxMajorIterations(args.max_opt_iters)
opt.setHessianResetFreq(args.hessian_reset)
# Enter the optimization loop
for i in range(max_mma_iters):
filename = os.path.join(args.prefix, 'paropt%04d.out' % (i))
opt.setOutputFile(filename)
opt.setInitBarrierParameter(0.1)
opt.optimize()
# Write the solution out to a file
if i % args.output_freq == 0:
itr = max_mma_iters * ite + i
filename = os.path.join(args.prefix,
'levelset05_binary%04d.bstl' % (itr))
# Get the vector and convert it
vec = problem.convertPVecToVec(mma.getOptimizedPoint())
TMR.writeSTLToBin(filename, filtr, vec)
# Get the optimized point
x, z, zw, zl, zu = opt.getOptimizedPoint()
mma.setMultipliers(z, zw)
mma.initializeSubProblem(x)
opt.resetDesignAndBounds()
# Compute the KKT error
l1_norm, linfty_norm, infeas = mma.computeKKTError()
if comm.rank == 0:
print 'z = ', z
print 'l1_norm = ', l1_norm
print 'infeas = ', infeas
if l1_norm < 1e-3 and infeas < 1e-6:
