def __call__(self, mtx_a, mtx_b=None, n_eigs=None,
eigenvectors=None, status=None, conf=None):
from pysparse import jdsym, itsolvers, precon
output("loading...")
A = self._convert_mat(mtx_a)
output("...done")
if mtx_b is not None:
M = self._convert_mat(mtx_b)
output("solving...")
Atau=A.copy()
Atau.shift(-conf.tau,M)
K=precon.jacobi(Atau)
A=A.to_sss();
if mtx_b is not None:
M=M.to_sss();
method = getattr(itsolvers, conf.method)
kconv, lmbd, Q, it, it_in = jdsym.jdsym(A, M, K, n_eigs, conf.tau,
conf.eps_a, conf.i_max,
method,
clvl=conf.verbosity,
strategy=conf.strategy)
output("number of converged eigenvalues:", kconv)
output("...done")
if status is not None:
status['q'] = Q
status['it'] = it
status['it_in'] = it_in
return lmbd, Q
def solve(A, B, n_eigs=4, verbose=False):
"""
Solves the generalized eigenvalue problem.
A, B ... scipy matrices
returns (lmbd, Q), where lmbd are the eigenvalues and Q is a numpy array of
solutions
"""
if verbose:
print "converting to pysparse"
n = A.shape[0]
A = convert_mat(A)
B = convert_mat(B)
if verbose:
print "solving (%d x %d)" % (n, n)
Atau = A.copy()
tau = -1
Atau.shift(-tau, B)
K = precon.jacobi(Atau)
A = A.to_sss()
B = B.to_sss()
kconv, lmbd, Q, it, it_in = jdsym.jdsym(A, B, K, n_eigs, tau, 1e-6, 150,
itsolvers.qmrs)
if verbose:
print "number of converged eigenvalues:", kconv
return lmbd, Q
def _solve_(self, L, x, b):
A = L._getMatrix().to_csr()
Assor=precon.jacobi(L._getMatrix())
info, iter, relres = itsolvers.gmres(A, b, x, self.tolerance, self.iterations, Assor)
self._raiseWarning(info, iter, relres)
def modalSolver(self, nev = 3, ncv = -1, tol = -1, mxiter = -1):
if SOLVER == 'default':
if ncv < 0:
ncv = 4*nev
ncv = ncv + nev
if tol < 0.:
tol = 1e-2
if mxiter < 0:
mxiter = 1000.
return solver.arpack(self.GK, self.GM, nev, ncv, tol, mxiter)
elif SOLVER == 'pysparse':
# copy matrixes
GK = self.GK
A = spmatrix.ll_mat_sym(self.dofcount, len(GK))
# TODO change to scipy
for row, col in GK:
A[row,col] = GK[row,col]
GM = self.GM
M = spmatrix.ll_mat_sym(self.dofcount, len(GM))
# TODO change to scipy
for row, col in GM:
M[row,col] = GM[row,col]
# Atau = A + tau*M
tau = -1.0
Atau = A.copy()
Atau.shift(tau, M)
K = precon.jacobi(Atau)
# convert to skyline
A = A.to_sss()
M = M.to_sss()
# solve
k_conv, lmbd, Q, it, it_innser = \
jdsym.jdsym(A, M, K, nev, tau,
1e-6, 1000, itsolvers.qmrs,
jmin=5, jmax=10, clvl=0, strategy=1)
# eigen values
return lmbd
else:
raise FEError('unknown solver')
def modalSolver(self, nev=3, ncv=-1, tol=-1, mxiter=-1):
if SOLVER == 'default':
if ncv < 0:
ncv = 4 * nev
ncv = ncv + nev
if tol < 0.:
tol = 1e-2
if mxiter < 0:
mxiter = 1000.
return solver.arpack(self.GK, self.GM, nev, ncv, tol, mxiter)
elif SOLVER == 'pysparse':
# copy matrixes
GK = self.GK
A = spmatrix.ll_mat_sym(self.dofcount, len(GK))
for row, col in GK:
A[row, col] = GK[row, col]
GM = self.GM
M = spmatrix.ll_mat_sym(self.dofcount, len(GM))
for row, col in GM:
M[row, col] = GM[row, col]
# Atau = A + tau*M
tau = -1.0
Atau = A.copy()
Atau.shift(tau, M)
K = precon.jacobi(Atau)
# convert to skyline
A = A.to_sss()
M = M.to_sss()
# solve
k_conv, lmbd, Q, it, it_innser = \
jdsym.jdsym(A, M, K, nev, tau,
1e-6, 1000, itsolvers.qmrs,
jmin=5, jmax=10, clvl=0, strategy=1)
# eigen values
return lmbd
else:
raise FEError('unknown solver')
def __call__( self, mtx_a, mtx_b = None, n_eigs = None,
eigenvectors = None, status = None, conf = None ):
from pysparse import jdsym, itsolvers, precon
conf = get_default( conf, self.conf )
mtx_a = get_default( mtx_a, self.mtx_a )
mtx_b = get_default( mtx_b, self.mtx_b )
n_eigs = get_default( n_eigs, self.n_eigs )
eigenvectors = get_default( eigenvectors, self.eigenvectors )
status = get_default( status, self.status )
output( "loading..." )
A = self._convert_mat( mtx_a )
output( "...done" )
if mtx_b is not None:
M = self._convert_mat( mtx_b )
output( "solving..." )
tt = time.clock()
Atau=A.copy()
Atau.shift(-conf.tau,M)
K=precon.jacobi(Atau)
A=A.to_sss();
if mtx_b is not None:
M=M.to_sss();
method = getattr( itsolvers, conf.method )
kconv, lmbd, Q, it, it_in = jdsym.jdsym( A, M, K, n_eigs, conf.tau,
conf.eps_a, conf.i_max,
method,
clvl = conf.verbosity,
strategy = conf.strategy )
output( "number of converged eigenvalues:", kconv )
ttt = time.clock() - tt
output( '...done in %.2f s' % ttt )
if status is not None:
status['time'] = ttt
status['q'] = Q
status['it'] = it
status['it_in'] = it_in
return lmbd, Q
def solve_eig_pysparse(A, B, n_eigs=4, verbose=False):
"""
Solves the generalized eigenvalue problem.
A, B ..... scipy matrices
n_eigs ... number of eigenvalues to solve for
returns a list of (lmbd, vec), where lmbd is the eigenvalue and vec is the
eigenvector
"""
from pysparse import jdsym, precon, itsolvers
if verbose:
print "converting to pysparse"
n = A.shape[0]
A = convert_mat(A)
B = convert_mat(B)
if verbose:
print "solving (%d x %d)" % (n, n)
Atau = A.copy()
tau = -1
Atau.shift(-tau, B)
K = precon.jacobi(Atau)
A = A.to_sss()
B = B.to_sss()
kconv, lmbd, Q, it, it_in = jdsym.jdsym(A, B, K, n_eigs, tau, 1e-6, 150,
itsolvers.qmrs)
if verbose:
print "number of converged eigenvalues:", kconv
r = []
for i in range(len(lmbd)):
vec = Q[:, i]
r.append((lmbd[i], vec))
r.sort(key=lambda x: x[0])
print "eigenvalues:"
eigs = []
for w, vec in r:
if w > 0:
break
print w
eigs.append(vec)
return r
def solve_eig_pysparse(A, B, n_eigs=4, verbose=False):
"""
Solves the generalized eigenvalue problem.
A, B ..... scipy matrices
n_eigs ... number of eigenvalues to solve for
returns a list of (lmbd, vec), where lmbd is the eigenvalue and vec is the
eigenvector
"""
from pysparse import jdsym, precon, itsolvers
if verbose:
print "converting to pysparse"
n = A.shape[0]
A = convert_mat(A)
B = convert_mat(B)
if verbose:
print "solving (%d x %d)" % (n, n)
Atau = A.copy()
tau = -1
Atau.shift(-tau, B)
K = precon.jacobi(Atau)
A = A.to_sss()
B = B.to_sss()
kconv, lmbd, Q, it, it_in = jdsym.jdsym(A, B, K, n_eigs, tau, 1e-6, 150,
itsolvers.qmrs)
if verbose:
print "number of converged eigenvalues:", kconv
r = []
for i in range(len(lmbd)):
vec = Q[:, i]
r.append((lmbd[i], vec))
r.sort(key=lambda x: x[0])
print "eigenvalues:"
eigs = []
for w, vec in r:
if w > 0:
break
print w
eigs.append(vec)
return r
import os
from pysparse import spmatrix, itsolvers, jdsym, precon
path = os.path.join(os.environ['HOME'], 'matrices')
A = spmatrix.ll_mat_from_mtx(os.path.join(path, 'edge6x3x5_A.mtx'))
M = spmatrix.ll_mat_from_mtx(os.path.join(path, 'edge6x3x5_B.mtx'))
sigma = 25.0
Asigma = A.copy()
Asigma.shift(-sigma, M)
K = precon.jacobi(Asigma.to_sss())
del Asigma
##n = A.shape[0]
##I = spmatrix.ll_mat(n, n)
##for i in xrange(n):
## I[i,i] = 1.0
##K = precon.jacobi(I)
k_conv, lmbd, Q, it, it_inner = \
jdsym.jdsym(A.to_sss(), M.to_sss(), K, 5, sigma, 1e-10, 150, itsolvers.qmrs,
jmin=5, jmax=10, eps_tr=1e-4, toldecay=2.0, linitmax=200, clvl=1, strategy=1)
print k_conv, lmbd, it, it_inner
##path = '/homes/geus/jdbsym/test/'
##A = spmatrix.ll_mat_from_mtx(path + 'sameh_10000.mtx').to_sss()
##K = precon.ssor(A)
##k_conv, lmbd, Q, it, it_inner = jdsym.jdsym(A, None, K, 4, 0.0, 1e-10, 50, itsolvers.qmrs,
## jmax=20, eps_tr=1e-4, toldecay=2.0, linitmax=60, clvl=1)
if test == 1:
# Test: compare K=eye with K=None
#
# results are not the same, ritz-value in 2nd iterations differ
path = '/local/home/geus/matrices'
A = spmatrix.ll_mat_from_mtx(path + 'edge6x3x5_A.mtx')
M = spmatrix.ll_mat_from_mtx(path + 'edge6x3x5_B.mtx')
n = A.shape[0]
sigma = 25.0
I = spmatrix.ll_mat(n, n)
for i in xrange(n):
I[i,i] = 1.0
Keye = precon.jacobi(I)
k_conv, lmbd, Q, it, it_inner = jdsym.jdsym(A.to_sss(), M.to_sss(), None, 5, sigma, 1e-10, 15, itsolvers.qmrs,
jmin=5, jmax=10, eps_tr=1e-4, toldecay=2.0, linitmax=200, clvl=1, strategy=1)
k_conv, lmbd, Q, it, it_inner = jdsym.jdsym(A.to_sss(), M.to_sss(), Keye, 5, sigma, 1e-10, 15, itsolvers.qmrs,
jmin=5, jmax=10, eps_tr=1e-4, toldecay=2.0, linitmax=200, clvl=1, strategy=1)
elif test == 2:
# Test 2: K = diag(A - sigma*M), test diagonal prec using Matlab
Asigma = A.copy()
Asigma.shift(-sigma, M)
K = precon.jacobi(Asigma.to_sss())
def _applyToMatrix(self, A):
"""
Returns (preconditioning matrix, resulting matrix)
"""
return precon.jacobi(A), A.to_csr()
def _applyToMatrix(self, A):
"""
Returns (preconditioning matrix, resulting matrix)
"""
return precon.jacobi(A), A.to_csr()
self.shape = A.shape
n = self.shape[0]
self.dinv = Numeric.zeros(n, 'd')
for i in xrange(n):
self.dinv[i] = 1.0 / A[i,i]
def precon(self, x, y):
Numeric.multiply(x, self.dinv, y)
def resid(A, b, x):
r = x.copy()
A.matvec(x, r)
r = b - r
return math.sqrt(Numeric.dot(r, r))
K_diag = diag_prec(A)
K_jac = precon.jacobi(A, 1.0, 1)
K_ssor = precon.ssor(A, 1.0, 1)
# K_ilu = precon.ilutp(L)
n = L.shape[0];
b = Numeric.arange(n).astype(Numeric.Float)
x = Numeric.zeros(n, 'd')
info, iter, relres = itsolvers.pcg(A, b, x, 1e-6, 1000)
print 'pcg, K_none: ', info, iter, relres, resid(A, b, x)
x = Numeric.zeros(n, 'd')
info, iter, relres = itsolvers.pcg(A, b, x, 1e-6, 1000, K_diag)
print 'pcg, K_diag: ', info, iter, relres, resid(A, b, x)
x = Numeric.zeros(n, 'd')
info, iter, relres = itsolvers.pcg(A, b, x, 1e-6, 1000, K_jac)
print 'pcg, K_jac: ', info, iter, relres, resid(A, b, x)
x = Numeric.zeros(n, 'd')
