from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.measure import Measure
# Define new algorithm.
IPOPT = Algorithm(name='IPOPT', description='Interior Point for OPTimization')
# Register executable for IPOPT.
IPOPT.set_executable_command('python ipopt_run.py')
# Define parameters.
# 5. Line search parameters
IPOPT.add_param(Parameter(name='tau_min',
kind='real',
bound=[0, 1],
default=0.99,
description='For fraction-to-boundary rule'))
IPOPT.add_param(Parameter(name='s_theta',
kind='real',
bound=[0, None],
default=1.1,
description='Exponent for current constraint ' +\
'violation'))
IPOPT.add_param(Parameter(name='s_phi',
kind='real',
bound=[0, None],
default=2.3,
description='Exponent for linear barrier function ' +\
'model'))
IPOPT.add_param(Parameter(name='delta',
# Description of the foward finite-difference "algorithm".
from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.measure import Measure
# Define Algorithm object.
LAS = Algorithm(name='LAS', description='Lattice Siever')
# Register executable for LAS.
LAS.set_executable_command('python las_run.py')
# Register parameter file used by black-box solver to communicate with LAS.
#LAS.set_parameter_file('las.param') # Should be chosen automatically and hidden.
# Define parameter and register it with algorithm.
lim0 = Parameter(kind='integer',
default=lim0_def,
bound=(lim0_min, lim0_max),
name='lim0',
description='Factor base bound, side 0')
lim1 = Parameter(kind='integer',
default=lim1_def,
bound=(lim1_min, lim1_max),
name='lim1',
description='Factor base bound, side 1')
lpb0 = Parameter(kind='integer',
default=lpb0_def,
bound=(lpb0_min, lpb0_max),
name='lpb0',
description='Large prime bound, side 0')
lpb1 = Parameter(kind='integer',
# Description of the foward finite-difference "algorithm".
from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.measure import Measure
# Define Algorithm object.
FD = Algorithm(name='FD', description='Forward Finite Differences')
# Register executable for FD.
FD.set_executable_command('python fd_run.py')
# Register parameter file used by black-box solver to communicate with FD.
#FD.set_parameter_file('fd.param') # Should be chosen automatically and hidden.
# Define parameter and register it with algorithm.
h = Parameter(kind='real', default=0.5, bound=(0, None),
name='h', description='Step size')
FD.add_param(h)
# Define relevant measure and register with algorithm.
error = Measure(kind='real', name='ERROR', description='Error in derivative')
FD.add_measure(error)
# Description of TRUNK.
from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.parameter import ParameterConstraint
from opal.core.measure import Measure
# Define Algorithm object.
trunk = Algorithm(name='TRUNK',
description='Trust Region for UNConstrained problems')
# Register executable command.
trunk.set_executable_command('python trunk_run.py')
# Register parameters.
trunk.add_param(Parameter(name='eta1',
kind='real',
default=0.25,
bound=[0, 1],
description='Gradient scaling cut-off'))
trunk.add_param(Parameter(name='eta2',
kind='real',
default=0.75,
bound=[0,1],
description='Trust-region increase threashold'))
trunk.add_param(Parameter(name='gamma1',
kind='real',
default=0.5,
bound=[0,1],
description='Trust-region shrink factor'))
trunk.add_param(Parameter(name='gamma2',
kind='real',
# Description of ABySS.
from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.measure import Measure
kd = int(raw_input("k-default: "))
kl = int(raw_input("k-lower: "))
ku = int(raw_input("k-upper: "))
# Define Algorithm object.
AB = Algorithm(name='AB', description='ABySS')
# Register executable command.
AB.set_executable_command('python abyss_run.py')
# Define parameter and register it with algorithm.
#200k-test k = 30; 16, 48
k = Parameter(kind='integer',
default=kd,
bound=(kl, ku),
name='k',
description='Step size')
AB.add_param(k)
# Define relevant measure and register with algorithm.
n50 = Measure(kind='integer', name='N50', description='N50 value')
AB.add_measure(n50)
#error = Measure(kind='real', name='ERROR', description='Error in derivative')
#AB.add_measure(error)
from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.measure import Measure
# Define new algorithm.
DFO = Algorithm(name='DFO', description='Derivative-free Optimization')
# Register executable for DFO.
DFO.set_executable_command('python dfo_run.py')
# Define parameters.
nx = Parameter(kind='integer', default=1, name='NX')
maxit = Parameter(kind='integer', default=5000, name='MAXIT')
maxef = Parameter(kind='integer', default=9500, name='MAXNF')
stpcrtr = Parameter(kind='integer', default=2, name='STPCRTR')
delmin = Parameter(default=1.0e-4, name='DELMIN',bound=(1.0e-8,1.0e-3))
stpthr = Parameter(default=1.0e-3, name='STPTHR',bound=(0,None))
cnstol = Parameter(default=1.0e-5, name='CNSTOL',bound=(0,0.1))
delta = Parameter(default=1.0e0, name='DELTA',bound=(1.0e-8,None))
pp = Parameter(default=1.0e0, name='PP',bound=(1,None))
scale = Parameter(kind='integer', default=0, name='SCALE')
iprint = Parameter(kind='integer', default=1, name='IPRINT')
# Register parameters with algorithm.
DFO.add_param(nx)
DFO.add_param(maxit)
DFO.add_param(maxef)
DFO.add_param(stpcrtr)
DFO.add_param(delmin)
DFO.add_param(stpthr)
DFO.add_param(cnstol)
# Description of the foward finite-difference "algorithm".
from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.measure import Measure
# Define Algorithm object.
FD = Algorithm(name='FD', description='Forward Finite Differences')
# Register executable for FD.
FD.set_executable_command('python fd_run.py')
# Register parameter file used by black-box solver to communicate with FD.
#FD.set_parameter_file('fd.param') # Should be chosen automatically and hidden.
# Define parameter and register it with algorithm.
h = Parameter(kind='real',
default=0.5,
bound=(0, None),
name='h',
description='Step size')
FD.add_param(h)
# Define relevant measure and register with algorithm.
error = Measure(kind='real', name='ERROR', description='Error in derivative')
FD.add_measure(error)
from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.measure import Measure
# Define Algorithm object.
NN = Algorithm(name="hyperNN", description="Hyperparameter optimisation")
# Register executable for NN.
NN.set_executable_command("python hyperMads/runner.py")
# Register parameter file used by black-box solver to communicate with NN.
# NN.set_parameter_file("fd.param")
# Should be chosen automatically and hidden.
# Define parameter and register it with algorithm.
# ac = Parameter(kind="categorical", default="relu",
# name="ac", description='Activation',
# neighbors={"relu": ["tanh", "sigmoid"],
# "tanh": ["relu", "sigmoid"],
# "sigmoid": ["relu", "tavnh"]})
# NN.add_param(ac)
# nv = Parameter(kind="categorical", default="True",
# name="nv", description="Nesterov",
# neighbors={"True": ["False"],
# "False": ["True"]})
# NN.add_param(nv)
lr = Parameter(kind="real",
default=.001,
bound=(0., 1.),
name="learning_rate",
# Description of the foward finite-difference "algorithm".
from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.measure import Measure
# Define Algorithm object.
FD = Algorithm(name='FD', description='tabu tenure')
# Register executable for FD.
FD.set_executable_command('python tabu_run.py')
# Register parameter file used by black-box solver to communicate with FD.
#FD.set_parameter_file('fd.param') # Should be chosen automatically and hidden.
# Define parameter and register it with algorithm.
longueur_T = Parameter(kind='integer', default=14, bound=(0, None),
name='longueur_T', description='Tabu tenure')
FD.add_param(longueur_T)
# Define relevant measure and register with algorithm.
error = Measure(kind='real', name='ERROR', description='Error in theta')
FD.add_measure(error)
from opal.core.algorithm import Algorithm
from opal.core.parameter import Parameter
from opal.core.measure import Measure
# Define new algorithm.
coopsort = Algorithm(name='CoopSort', description='Sort Algorithm')
# Register executable.
coopsort.set_executable_command('python coopsort_run.py')
# Define parameters.
# The following coop tree amounts to 5522522 (in base 6.)
coopsort.add_param(Parameter(name='coopTree',
kind='categorical',
default=275378,
#default=284354431,
description='Encoded cooperation tree'))
# This dummy parameter is just there to circumvent a bug in NOMAD
# that occurs when the problem has a single parameter and this parameter
# is categorical.
coopsort.add_param(Parameter(name='nothing',
kind='integer',
default=0,
description='To avoid a bug in NOMAD'))
coopsort.add_measure(Measure(name='TIME',
kind='real',
description='Computing time'))
