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)