def __call__(self, function, point, state):
"""
Computes Goldstein-Price step
"""
g = function.gradient(point)
state["gradient"] = g
G = function.hessian(point)
state["hessian"] = G
isPositiveDefinite = True
d0 = None
try:
L = cholesky(G)
d0 = n_solve(L.T, n_solve(L, -g))
except:
isPositiveDefinite = False
if isPositiveDefinite:
cosTheta = dot(d0, -g) / (norm(d0) * norm(g))
if cosTheta >= self.nu:
step = d0
else:
step = -g
else:
step = -g
state["direction"] = step
return step
def __call__(self, function, point, state):
"""
Computes Goldstein-Price step
"""
g = function.gradient(point)
state['gradient'] = g
G = function.hessian(point)
state['hessian'] = G
isPositiveDefinite = True
d0 = None
try:
L = cholesky(G)
d0 = n_solve(L.T, n_solve(L, -g))
except:
isPositiveDefinite = False
if isPositiveDefinite:
cosTheta = dot(d0, -g) / (norm(d0) * norm(g))
if cosTheta >= self.nu:
step = d0
else:
step = -g
else:
step = -g
state['direction'] = step
return step
def __call__(self, function, point, state):
"""
Computes Goldfeld step
"""
g = function.gradient(point)
state['gradient'] = g
G = function.hessian(point)
state['hessian'] = G
c = 1e-8 # is this one best?
d0 = None
try:
L = cholesky(G)
# reach here => isPositiveDefinite = True
step = n_solve(L.T, n_solve(L, -g))
except:
# isPositiveDefinite = False
G_eigvals = eigvalsh(G)
minEig = min(G_eigvals)
if minEig < 0:
shift = -minEig + c
#avoiding sparse case with big nVars
for i in xrange(point): G[i,i] += shift
step = n_solve(G, -g)
state['direction'] = step
return step
def __call__(self, function, point, state):
"""
Computes Goldfeld step
"""
g = function.gradient(point)
state['gradient'] = g
G = function.hessian(point)
state['hessian'] = G
c = 1e-8 # is this one best?
d0 = None
try:
L = cholesky(G)
# reach here => isPositiveDefinite = True
step = n_solve(L.T, n_solve(L, -g))
except:
# isPositiveDefinite = False
G_eigvals = eigvalsh(G)
minEig = min(G_eigvals)
if minEig < 0:
shift = -minEig + c
#avoiding sparse case with big nVars
for i in xrange(point):
G[i, i] += shift
step = n_solve(G, -g)
state['direction'] = step
return step
