def para_solver(L):
# Levenberg-Marquardt
# HT = H + (L-1)**2*np.diag(np.diag(H))
# Attempt to use plain Levenberg
HT = H + (L-1)**2*np.eye(len(H))
# print "Inverting Scaled Hessian:" ###
# print " G:" ###
# pvec1d(G,precision=5) ###
# print " HT: (Scal = %.4f)" % (1+(L-1)**2) ###
# pmat2d(HT,precision=5) ###
Hi = invert_svd(np.mat(HT))
dx = flat(-1 * Hi * col(G))
# print " dx:" ###
# pvec1d(dx,precision=5) ###
# dxa = -solve(HT, G)
# dxa = flat(dxa)
# print " dxa:" ###
# pvec1d(dxa,precision=5) ###
# print ###
sol = flat(0.5*row(dx)*np.mat(H)*col(dx))[0] + np.dot(dx,G)
for i in self.excision: # Reinsert deleted coordinates - don't take a step in those directions
dx = np.insert(dx, i, 0)
return dx, sol
def _compute(self, dx):
self.dx = dx.copy()
Tmp = np.mat(self.H)*col(dx)
Reg_Term = self.Penalty.compute(xkd+flat(dx), Obj0)
self.Val = (X + np.dot(dx, G) + 0.5*row(dx)*Tmp + Reg_Term[0] - data['X'])[0,0]
self.Grad = flat(col(G) + Tmp) + Reg_Term[1]