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 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_function(cls):
m = loadnpy('m_try')
f = problem.func(m)
savetxt('f_try', f)
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_function(cls):
m = loadnpy('m_try')
f = problem.func(m)
savetxt('f_try',f)
