def test_spg_optim(self):
dm = 100
ind = np.arange(dm)
indmat = np.abs(ind[:,None] - ind[None,:])
M = 0.8**indmat
def obj(x):
return np.dot(x, np.dot(M, x))
def grad(x):
return 2*np.dot(M, x)
def project(x):
return x
x = np.random.normal(size=dm)
rslt = _spg_optim(obj, grad, x, project)
xnew = rslt.params
assert_almost_equal(obj(xnew), 0, decimal=3)
def test_spg_optim(self):
dm = 100
ind = np.arange(dm)
indmat = np.abs(ind[:,None] - ind[None,:])
M = 0.8**indmat
def obj(x):
return np.dot(x, np.dot(M, x))
def grad(x):
return 2*np.dot(M, x)
def project(x):
return x
x = np.random.normal(size=dm)
rslt = _spg_optim(obj, grad, x, project)
xnew = rslt.params
assert(obj(xnew) < 1e-4)
def test_spg_optim(self, reset_randomstate):
dm = 100
ind = np.arange(dm)
indmat = np.abs(ind[:, None] - ind[None, :])
M = 0.8**indmat
def obj(x):
return np.dot(x, np.dot(M, x))
def grad(x):
return 2 * np.dot(M, x)
def project(x):
return x
x = np.random.normal(size=dm)
rslt = _spg_optim(obj, grad, x, project)
xnew = rslt.params
assert rslt.Converged is True
assert_almost_equal(obj(xnew), 0, decimal=3)
