def setup(self):
""" Sets up nonlinear optimization machinery
"""
unix.mkdir(PATH.OPTIMIZE)
# prepare output writers
self.writer = Writer(path=PATH.OUTPUT)
self.stepwriter = StepWriter(path=PATH.SUBMIT)
# prepare algorithm machinery
if PAR.SCHEME in ['NLCG']:
self.NLCG = NLCG(path=PATH.OPTIMIZE,
maxiter=PAR.NLCGMAX,
thresh=PAR.NLCGTHRESH,
precond=self.precond())
elif PAR.SCHEME in ['LBFGS']:
self.LBFGS = LBFGS(path=PATH.OPTIMIZE,
memory=PAR.LBFGSMEM,
maxiter=PAR.LBFGSMAX,
thresh=PAR.LBFGSTHRESH,
precond=self.precond())
# write initial model
if exists(PATH.MODEL_INIT):
import solver
src = PATH.MODEL_INIT
dst = join(PATH.OPTIMIZE, 'm_new')
savenpy(dst, solver.merge(solver.load(src)))
def apply_precond(self, r):
if not self.precond:
return r
elif self.precond in ['LBFGS_3']:
if self.iter == 1:
self.LBFGS = LBFGS(self.path, memory=3)
y = r
elif self.ilcg == 0:
S, Y = self.LBFGS.update()
y = -self.LBFGS.apply(self.load('LCG/r'), S, Y)
else:
y = -self.LBFGS.apply(self.load('LCG/r'))
return y
elif self.precond in ['LBFGS_6']:
if self.iter == 1:
self.LBFGS = LBFGS(self.path, memory=6)
y = r
elif self.ilcg == 0:
S, Y = self.LBFGS.update()
y = -self.LBFGS.apply(self.load('LCG/r'), S, Y)
else:
y = -self.LBFGS.apply(self.load('LCG/r'))
return y
elif self.precond in ['LBFGS_9']:
if self.iter == 1:
self.LBFGS = LBFGS(self.path, memory=9)
y = r
elif self.ilcg == 0:
S, Y = self.LBFGS.update()
y = -self.LBFGS.apply(self.load('LCG/r'), S, Y)
else:
y = -self.LBFGS.apply(self.load('LCG/r'))
return y
else:
raise ValueError
