def setupLASolver():
lasolver = pyTrilinos.daeCreateTrilinosSolver("AztecOO", "")
parameterList = lasolver.ParameterList
lasolver.NumIters = 1000
lasolver.Tolerance = 1e-3
parameterList.set_int("AZ_solver", daeAztecOptions.AZ_gmres)
parameterList.set_int("AZ_kspace", 500)
parameterList.set_int("AZ_scaling", daeAztecOptions.AZ_none)
parameterList.set_int("AZ_reorder", 0)
parameterList.set_int("AZ_conv", daeAztecOptions.AZ_r0)
parameterList.set_int("AZ_keep_info", 1)
parameterList.set_int(
"AZ_output", daeAztecOptions.AZ_none
) # {AZ_all, AZ_none, AZ_last, AZ_summary, AZ_warnings}
# Preconditioner options
parameterList.set_int("AZ_precond", daeAztecOptions.AZ_dom_decomp)
parameterList.set_int("AZ_subdomain_solve", daeAztecOptions.AZ_ilu)
parameterList.set_int("AZ_orthog", daeAztecOptions.AZ_modified)
parameterList.set_int("AZ_graph_fill", 1) # default: 0
parameterList.set_float("AZ_athresh", 1E-5) # default: 0.0
parameterList.set_float("AZ_rthresh", 1.0) # default: 0.0
parameterList.Print()
return lasolver
def run(**kwargs):
simulation = simTutorial()
print('Supported Trilinos solvers: %s' %
pyTrilinos.daeTrilinosSupportedSolvers())
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Klu", "")
return daeActivity.simulate(simulation,
reportingInterval=5,
timeHorizon=60,
lasolver=lasolver,
**kwargs)
def run(**kwargs):
simulation = simCatalystMixing(200, 1.0 / 200)
nlpsolver = pyIPOPT.daeIPOPT()
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Klu", "")
relativeTolerance = 1e-6
return daeActivity.optimize(simulation,
reportingInterval=1,
timeHorizon=1,
lasolver=lasolver,
nlpsolver=nlpsolver,
nlpsolver_setoptions_fn=setOptions,
relativeTolerance=relativeTolerance,
**kwargs)
def run(**kwargs):
simulation = simTutorial()
# The default linear solver is Sundials dense sequential solver (LU decomposition).
# The following 3rd party direct linear solvers are supported:
# - Pardiso (multi-threaded - OMP)
# - IntelPardiso (multi-threaded - OMP)
# - SuperLU (sequential)
# - SuperLU_MT (multi-threaded - pthreads, OMP)
# - Trilinos Amesos (sequential): Klu, SuperLU, Lapack, Umfpack
# If you are using Pardiso or IntelPardiso solvers you have to export their bin directories,
# using, for instance, LD_LIBRARY_PATH shell variable (for more details see their documentation).
# If you are using OpenMP capable solvers you should set the number of threads
# (typically to the number of cores), for instance:
# export OMP_NUM_THREADS=4
# or if using IntelPardiso solver:
# export MKL_NUM_THREADS=24
# You can place the above command at the end of $HOME/.bashrc (or type it in shell, before simulation).
# Import desired solver module (uncomment it from below) and set it using SetLASolver function:
print("Supported Trilinos 3rd party LA solvers:",
str(pyTrilinos.daeTrilinosSupportedSolvers()))
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Klu", "")
#lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Umfpack", "")
#lasolver = pyIntelPardiso.daeCreateIntelPardisoSolver()
#lasolver = pyPardiso.daeCreatePardisoSolver()
if lasolver.Name == 'Intel Pardiso':
# Get Pardiso/IntelPardiso parameters (iparm[64] list of integers)
iparm = lasolver.get_iparm()
iparm_def = list(iparm) # Save it for comparison
# Change some options
# Achtung, Achtung!!
# The meaning of items in iparm[64] is NOT identical in Pardiso and IntelPardiso solvers!!
iparm[
7] = 2 # Max. number of iterative refinement steps (common for both Pardiso and IntelPardiso)
#iparm[27] = 1 # in Pardiso it means: use METIS parallel reordering
# in IntelPardiso it means: use single precision (do not change it!)
# Set them back
lasolver.set_iparm(iparm)
iparm = lasolver.get_iparm()
# Print the side by side comparison
print('iparm default new')
for i in range(64):
print('iparm[%2d] %7d %3d' % (i, iparm_def[i], iparm[i]))
return daeActivity.simulate(simulation,
reportingInterval=10,
timeHorizon=1000,
lasolver=lasolver,
**kwargs)
def run(**kwargs):
simulation = simTutorial()
print("Supported Trilinos solvers: %s" %
pyTrilinos.daeTrilinosSupportedSolvers())
# lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Klu", "")
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Umfpack", "")
# lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Lapack", "")
# lasolver = pyTrilinos.daeCreateTrilinosSolver("'AztecOO_ML", "")
return daeActivity.simulate(
simulation,
reportingInterval=1,
timeHorizon=20 * 60,
lasolver=lasolver,
# calculateSensitivities=True,
**kwargs)
def run(**kwargs):
simulation = simOilCracking_opt()
nlpsolver = pyIPOPT.daeIPOPT()
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Klu", "")
relativeTolerance = 1e-6
reportingTimes = times.tolist()
return daeActivity.optimize(simulation,
reportingInterval=1,
timeHorizon=1,
reportingTimes=reportingTimes,
lasolver=lasolver,
nlpsolver=nlpsolver,
nlpsolver_setoptions_fn=setOptions,
relativeTolerance=relativeTolerance,
**kwargs)
def createLASolver():
print("Supported Trilinos solvers:", str(pyTrilinos.daeTrilinosSupportedSolvers()))
# Amesos SuperLU solver
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Umfpack", "")
# AztecOO built-in preconditioners are specified through AZ_precond option
#lasolver = pyTrilinos.daeCreateTrilinosSolver("AztecOO", "")
# Ifpack preconditioner can be one of: [ILU, ILUT, PointRelaxation, BlockRelaxation, IC, ICT]
#lasolver = pyTrilinos.daeCreateTrilinosSolver("AztecOO_Ifpack", "ILU")
# ML preconditioner can be one of: [SA, DD, DD-ML, DD-ML-LU, maxwell, NSSA]
#lasolver = pyTrilinos.daeCreateTrilinosSolver("AztecOO_ML", "DD-ML")
return lasolver
def run(**kwargs):
simulation = simMarinePopulation_opt()
#nlpsolver = pyNLOPT.daeNLOPT('NLOPT_LD_SLSQP')
nlpsolver = pyIPOPT.daeIPOPT()
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Klu", "")
relativeTolerance = 1e-6
reportingTimes = times.tolist()
return daeActivity.optimize(simulation,
reportingInterval=1,
timeHorizon=1,
reportingTimes=reportingTimes,
lasolver=lasolver,
nlpsolver=nlpsolver,
nlpsolver_setoptions_fn=setOptions,
relativeTolerance=relativeTolerance,
reportSensitivities=True,
**kwargs)
def createLASolver(lasolverIndex):
lasolver = None
if lasolverIndex == laSundialsLU:
pass
elif lasolverIndex == laSuperLU:
from daetools.solvers.superlu import pySuperLU
lasolver = pySuperLU.daeCreateSuperLUSolver()
elif lasolverIndex == laSuperLU_MT:
from daetools.solvers.superlu_mt import pySuperLU_MT
lasolver = pySuperLU_MT.daeCreateSuperLUSolver()
elif lasolverIndex == laAmesos_Klu:
from daetools.solvers.trilinos import pyTrilinos
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Klu", "")
elif lasolverIndex == laAmesos_Superlu:
from daetools.solvers.trilinos import pyTrilinos
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Superlu", "")
elif lasolverIndex == laAmesos_Umfpack:
from daetools.solvers.trilinos import pyTrilinos
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Umfpack", "")
elif lasolverIndex == laAmesos_Lapack:
from daetools.solvers.trilinos import pyTrilinos
lasolver = pyTrilinos.daeCreateTrilinosSolver("Amesos_Lapack", "")
elif lasolverIndex == laAztecOO:
from daetools.solvers.trilinos import pyTrilinos
lasolver = pyTrilinos.daeCreateTrilinosSolver("AztecOO", "ILUT")
elif lasolverIndex == laPardiso:
from daetools.solvers.pardiso import pyPardiso
lasolver = pyPardiso.daeCreatePardisoSolver()
elif lasolverIndex == laIntelPardiso:
from daetools.solvers.intel_pardiso import pyIntelPardiso
lasolver = pyIntelPardiso.daeCreateIntelPardisoSolver()
elif lasolverIndex == laIntelMKL:
from daetools.solvers import pyIntelMKL
lasolver = pyIntelMKL.daeCreateLapackSolver()
elif lasolverIndex == laAmdACML:
from daetools.solvers import pyAmdACML
lasolver = pyAmdACML.daeCreateLapackSolver()
elif lasolverIndex == laLapack:
from daetools.solvers import pyLapack
lasolver = pyLapack.daeCreateLapackSolver()
elif lasolverIndex == laMagmaLapack:
from daetools.solvers import pyMagma
lasolver = pyMagma.daeCreateLapackSolver()
elif lasolverIndex == laSuperLU_CUDA:
from daetools.solvers.superlu_cuda import pySuperLU_CUDA
lasolver = pySuperLU_CUDA.daeCreateSuperLUSolver()
elif lasolverIndex == laCUSP:
from daetools.solvers import pyCUSP
lasolver = pyCUSP.daeCreateCUSPSolver()
else:
raise RuntimeError("Unsupported LA Solver selected")
return lasolver
