def compute_direction_newton(cls):
optimize.initialize_newton()
for ilcg in range(PAR.LCGMAX):
m = loadnpy('m_lcg')
g = problem.grad(m)
savenpy('g_lcg', g)
isdone = optimize.iterate_newton()
if isdone:
break
def evaluate_gradient(cls):
m = loadnpy('m_new')
f = problem.func(m)
g = problem.grad(m)
savetxt('f_new', f)
savenpy('g_new', g)
if PAR.OPTIMIZE in ['SRVM']:
optimize.update_SRVM()
def compute_direction_newton(cls):
optimize.initialize_newton()
for ilcg in range(PAR.LCGMAX):
m = loadnpy('m_lcg')
g = problem.grad(m)
savenpy('g_lcg', g)
isdone = optimize.iterate_newton()
if isdone:
break
def evaluate_gradient(cls):
m = loadnpy('m_new')
f = problem.func(m)
g = problem.grad(m)
savetxt('f_new', f)
savenpy('g_new', g)
def evaluate_gradient(cls):
m = loadnpy('m_new')
f = problem.func(m)
g = problem.grad(m)
savetxt('f_new',f)
savenpy('g_new',g)