Ejemplo n.º 1
0
def test_mkl_spsolve6():
    """
    MKL splu : Repeated RHS solve (Complex)
    """
    row = np.array([0,0,1,2,2,2])
    col = np.array([0,2,2,0,1,2])
    data = np.array([1,2,3,-4,5,6], dtype=complex)
    sM = sp.csr_matrix((data,(row,col)), shape=(3,3), dtype=complex)
    M = sM.toarray()

    row = np.array([0,0,1,1,0,0])
    col = np.array([0,2,1,1,0,0])
    data = np.array([1,1,1,1,1,1], dtype=complex)
    sN = sp.csr_matrix((data, (row,col)), shape=(3,3), dtype=complex)
    N = sN.toarray()

    sX = np.zeros((3,3),dtype=complex)
    lu = mkl_splu(sM)

    for k in range(3):
        sX[:,k] = lu.solve(N[:,k])
    lu.delete()
    
    X = la.solve(M,N)
    assert_array_almost_equal(X, sX)
Ejemplo n.º 2
0
def _steadystate_power(L, ss_args):
    """
    Inverse power method for steady state solving.
    """
    ss_args['info'].pop('weight', None)
    if settings.debug:
        logger.debug('Starting iterative inverse-power method solver.')
    tol = ss_args['tol']
    maxiter = ss_args['maxiter']

    use_solver(assumeSortedIndices=True)
    rhoss = Qobj()
    sflag = issuper(L)
    if sflag:
        rhoss.dims = L.dims[0]
    else:
        rhoss.dims = [L.dims[0], 1]
    n = L.shape[0]
    # Build Liouvillian
    if settings.has_mkl and ss_args['method'] == 'power':
        has_mkl = 1
    else:
        has_mkl = 0
    L, perm, perm2, rev_perm, ss_args = _steadystate_power_liouvillian(L, 
                                                ss_args, has_mkl)
    orig_nnz = L.nnz
    # start with all ones as RHS
    v = np.ones(n, dtype=complex)
    if ss_args['use_rcm']:
        v = v[np.ix_(perm2,)]
    
    # Do preconditioning
    if ss_args['M'] is None and ss_args['use_precond'] and \
            ss_args['method'] in ['power-gmres', 
                                'power-lgmres', 'power-bicgstab']:
        ss_args['M'], ss_args = _iterative_precondition(L, int(np.sqrt(n)), ss_args)
        if ss_args['M'] is None:
            warnings.warn("Preconditioning failed. Continuing without.",
                          UserWarning)
    
    ss_iters = {'iter': 0}

    def _iter_count(r):
        ss_iters['iter'] += 1
        return
    
    _power_start = time.time()
    # Get LU factors
    if ss_args['method'] == 'power':
        if settings.has_mkl:
            lu = mkl_splu(L)
        else: 
            lu = splu(L, permc_spec=ss_args['permc_spec'],
              diag_pivot_thresh=ss_args['diag_pivot_thresh'],
              options=dict(ILU_MILU=ss_args['ILU_MILU']))

        if settings.debug and _scipy_check:
            L_nnz = lu.L.nnz
            U_nnz = lu.U.nnz
            logger.debug('L NNZ: %i ; U NNZ: %i' % (L_nnz, U_nnz))
            logger.debug('Fill factor: %f' % ((L_nnz+U_nnz)/orig_nnz))

    it = 0
    _tol = max(ss_args['tol']/10, 1e-15) # Should make this user accessible
    while (la.norm(L * v, np.inf) > tol) and (it < maxiter):
        
        if ss_args['method'] == 'power':
            v = lu.solve(v)
        elif ss_args['method'] == 'power-gmres':
            v, check = gmres(L, v, tol=_tol, M=ss_args['M'],
                                x0=ss_args['x0'], restart=ss_args['restart'],
                                maxiter=ss_args['maxiter'], callback=_iter_count)
        elif ss_args['method'] == 'power-lgmres':
            v, check = lgmres(L, v, tol=_tol, M=ss_args['M'],
                              x0=ss_args['x0'], maxiter=ss_args['maxiter'],
                              callback=_iter_count)
        elif ss_args['method'] == 'power-bicgstab':
            v, check = bicgstab(L, v, tol=_tol, M=ss_args['M'],
                                x0=ss_args['x0'],
                                maxiter=ss_args['maxiter'], callback=_iter_count)
        else:
            raise Exception("Invalid iterative solver method.")
            
        v = v / la.norm(v, np.inf)
        it += 1
    if ss_args['method'] == 'power' and settings.has_mkl:
        lu.delete()
    if it >= maxiter:
        raise Exception('Failed to find steady state after ' +
                        str(maxiter) + ' iterations')
    _power_end = time.time()
    ss_args['info']['solution_time'] = _power_end-_power_start
    ss_args['info']['iterations'] = it
    if ss_args['return_info']:
        ss_args['info']['residual_norm'] = la.norm(L*v)
    if settings.debug:
        logger.debug('Number of iterations: %i' % it)

    if ss_args['use_rcm']:
        v = v[np.ix_(rev_perm,)]

    # normalise according to type of problem
    if sflag:
        trow = sp.eye(rhoss.shape[0], rhoss.shape[0], format='coo')
        trow = sp_reshape(trow, (1, n))
        data = v / sum(trow.dot(v))
    else:
        data = data / la.norm(v)

    data = sp.csr_matrix(vec2mat(data))
    rhoss.data = 0.5 * (data + data.conj().T)
    rhoss.isherm = True
    if ss_args['return_info']:
        return rhoss, ss_args['info']
    else:
        return rhoss