def getStep(self, X, G):
"""
use the Fortran MYLBFGS code from GMIN to update the LBFGS memory and get the step size and direction.
"""
self.X = X
self.G = G
# save the position and gradient change
if self._iter > 0:
N = self.N
M = self.M
#
# need write the change in position and the change in gradient
# to W. This should be done more elegantly
#
ISPT= N + 2*M # index for storage of search steps
IYPT= ISPT + N*M # index for storage of gradient differences
NPT = N*((self._point + M - 1) % M)
# s = X - self.Xold
# y = G - self.Gold
self.W[ISPT+NPT : ISPT+NPT +N] = self.dXold
self.W[IYPT+NPT : IYPT+NPT +N] = self.dGold
self.stp = mylbfgs_updatestep(self._iter, self.M, G, self.W, self.H0vec, self._point)
self.H0 = self.H0vec[0]
self._iter += 1
self._point = self._iter % self.M
return self.stp
def getStep(self, X, G):
"""
use the Fortran MYLBFGS code from GMIN to update the LBFGS memory and get the step size and direction.
"""
self.X = X
self.G = G
# save the position and gradient change
if self._iter > 0:
N = self.N
M = self.M
#
# need write the change in position and the change in gradient
# to W. This should be done more elegantly
#
ISPT = N + 2 * M # index for storage of search steps
IYPT = ISPT + N * M # index for storage of gradient differences
NPT = N * ((self._point + M - 1) % M)
# s = X - self.Xold
# y = G - self.Gold
self.W[ISPT + NPT:ISPT + NPT + N] = self.dXold
self.W[IYPT + NPT:IYPT + NPT + N] = self.dGold
self.stp = mylbfgs_updatestep(self._iter, self.M, G, self.W,
self.H0vec, self._point)
self.H0 = self.H0vec[0]
self._iter += 1
self._point = self._iter % self.M
return self.stp
def getStep(self, X, G):
"""
use the Fortran LBFGS code from GMIN to get the step
size and direction.
"""
self.X = X
self.G = G
#save the position and gradient change
if self.iter > 0:
N = self.N
M = self.M
#
# need write the change in position and the change in gradient
# to W. This should be done more elegantly
#
ISPT= N + 2*M # index for storage of search steps
IYPT= ISPT + N*M # index for storage of gradient differences
NPT = N*((self.point + M - 1) % M)
#s = X - self.Xold
#y = G - self.Gold
#print "YS YY py", np.dot( y, s ), np.dot( y,y ), ISPT+NPT
self.W[ISPT+NPT : ISPT+NPT +N] = X - self.Xold
self.W[IYPT+NPT : IYPT+NPT +N] = G - self.Gold
self.Xold = X.copy()
self.Gold = G.copy()
#print self.iter, self.point
self.stp = mylbfgs_updatestep(self.iter, self.M, G, self.W, self.H0vec, self.point, [self.N])
#print "stp", np.linalg.norm(self.stp), self.stp
#print "G", self.G
#print "overlap", np.dot(self.stp, self.G)
#print "H0", self.H0
self.H0 = self.H0vec[0]
self.iter += 1
self.point = self.iter % self.M
return self.stp
def getStep(self, X, G):
"""
use the Fortran LBFGS code from GMIN to get the step
size and direction.
"""
self.X = X
self.G = G
#save the position and gradient change
if self.iter > 0:
N = self.N
M = self.M
#
# need write the change in position and the change in gradient
# to W. This should be done more elegantly
#
ISPT = N + 2 * M # index for storage of search steps
IYPT = ISPT + N * M # index for storage of gradient differences
NPT = N * ((self.point + M - 1) % M)
#s = X - self.Xold
#y = G - self.Gold
#print "YS YY py", np.dot( y, s ), np.dot( y,y ), ISPT+NPT
self.W[ISPT + NPT:ISPT + NPT + N] = X - self.Xold
self.W[IYPT + NPT:IYPT + NPT + N] = G - self.Gold
self.Xold = X.copy()
self.Gold = G.copy()
#print self.iter, self.point
self.stp = mylbfgs_updatestep(self.iter, self.M, G, self.W, self.H0vec,
self.point, [self.N])
#print "stp", np.linalg.norm(self.stp), self.stp
#print "G", self.G
#print "overlap", np.dot(self.stp, self.G)
#print "H0", self.H0
self.H0 = self.H0vec[0]
self.iter += 1
self.point = self.iter % self.M
return self.stp
