def test_j_roots():
rf = lambda a, b: lambda n, mu: orth.j_roots(n, a, b, mu)
ef = lambda a, b: lambda n, x: orth.eval_jacobi(n, a, b, x)
wf = lambda a, b: lambda x: (1 - x) ** a * (1 + x) ** b
vgq = verify_gauss_quad
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1.0, 1.0, 5)
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1.0, 1.0, 25, atol=1e-12)
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1.0, 1.0, 100, atol=1e-11)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1.0, 1.0, 5)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1.0, 1.0, 25, atol=1.5e-13)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1.0, 1.0, 100, atol=1e-12)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1.0, 1.0, 5, atol=2e-13)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1.0, 1.0, 25, atol=2e-13)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1.0, 1.0, 100, atol=1e-12)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1.0, 1.0, 5)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1.0, 1.0, 25, atol=1e-13)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1.0, 1.0, 100, atol=2e-13)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1.0, 1.0, 5)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1.0, 1.0, 25)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1.0, 1.0, 100, atol=1e-13)
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1.0, 1.0, 5, atol=1e-13)
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1.0, 1.0, 25, atol=2e-13)
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1.0, 1.0, 100, atol=1e-11)
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1.0, 1.0, 5)
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1.0, 1.0, 25, atol=1e-13)
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1.0, 1.0, 100, atol=1e-13)
# when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
xj, wj = orth.j_roots(6, 0.0, 0.0)
xl, wl = orth.p_roots(6)
assert_allclose(xj, xl, 1e-14, 1e-14)
assert_allclose(wj, wl, 1e-14, 1e-14)
# when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
xj, wj = orth.j_roots(6, 4.0, 4.0)
xc, wc = orth.cg_roots(6, 4.5)
assert_allclose(xj, xc, 1e-14, 1e-14)
assert_allclose(wj, wc, 1e-14, 1e-14)
x, w = orth.j_roots(5, 2, 3, False)
y, v, m = orth.j_roots(5, 2, 3, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
muI, muI_err = integrate.quad(wf(2, 3), -1, 1)
assert_allclose(m, muI, rtol=muI_err)
assert_raises(ValueError, orth.j_roots, 0, 1, 1)
assert_raises(ValueError, orth.j_roots, 3.3, 1, 1)
assert_raises(ValueError, orth.j_roots, 3, -2, 1)
assert_raises(ValueError, orth.j_roots, 3, 1, -2)
assert_raises(ValueError, orth.j_roots, 3, -2, -2)
def test_j_roots():
roots = lambda a, b: lambda n, mu: orth.j_roots(n, a, b, mu)
evalf = lambda a, b: lambda n, x: orth.eval_jacobi(n, a, b, x)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 5)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 25, atol=1e-12)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 100, atol=1e-11)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 5)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 25, atol=1e-13)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 100, atol=1e-12)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 5, atol=2e-13)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 25, atol=2e-13)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 100, atol=1e-12)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 5)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 25, atol=1e-13)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 100, atol=2e-13)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 5)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 25)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 100, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 5, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 25, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 100, atol=1e-11)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 5)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 25, atol=1e-13)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 100, atol=1e-13)
# when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
xj, wj = orth.j_roots(6, 0.0, 0.0)
xl, wl = orth.p_roots(6)
assert_allclose(xj, xl, 1e-14, 1e-14)
assert_allclose(wj, wl, 1e-14, 1e-14)
# when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
xj, wj = orth.j_roots(6, 4.0, 4.0)
xc, wc = orth.cg_roots(6, 4.5)
assert_allclose(xj, xc, 1e-14, 1e-14)
assert_allclose(wj, wc, 1e-14, 1e-14)
x, w = orth.j_roots(5, 2, 3, False)
y, v, m = orth.j_roots(5, 2, 3, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
assert_raises(ValueError, orth.j_roots, 0, 1, 1)
assert_raises(ValueError, orth.j_roots, 3.3, 1, 1)
assert_raises(ValueError, orth.j_roots, 3, -2, 1)
assert_raises(ValueError, orth.j_roots, 3, 1, -2)
assert_raises(ValueError, orth.j_roots, 3, -2, -2)
def test_j_roots():
roots = lambda a, b: lambda n, mu: orth.j_roots(n, a, b, mu)
evalf = lambda a, b: lambda n, x: orth.eval_jacobi(n, a, b, x)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 5)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 25, atol=1e-12)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 100, atol=1e-11)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 5)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 25, atol=1e-13)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 100, atol=1e-12)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 5, atol=2e-13)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 25, atol=2e-13)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 100, atol=1e-12)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 5)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 25, atol=1e-13)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 100, atol=2e-13)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 5)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 25)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 100, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 5, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 25, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 100, atol=1e-11)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 5)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 25, atol=1e-13)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 100, atol=1e-13)
# when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
xj, wj = orth.j_roots(6, 0.0, 0.0)
xl, wl = orth.p_roots(6)
assert_allclose(xj, xl, 1e-14, 1e-14)
assert_allclose(wj, wl, 1e-14, 1e-14)
# when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
xj, wj = orth.j_roots(6, 4.0, 4.0)
xc, wc = orth.cg_roots(6, 4.5)
assert_allclose(xj, xc, 1e-14, 1e-14)
assert_allclose(wj, wc, 1e-14, 1e-14)
x, w = orth.j_roots(5, 2, 3, False)
y, v, m = orth.j_roots(5, 2, 3, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
assert_raises(ValueError, orth.j_roots, 0, 1, 1)
assert_raises(ValueError, orth.j_roots, 3.3, 1, 1)
assert_raises(ValueError, orth.j_roots, 3, -2, 1)
assert_raises(ValueError, orth.j_roots, 3, 1, -2)
assert_raises(ValueError, orth.j_roots, 3, -2, -2)
def setQuadrature(self,maxOrder,verbose=False):
super(Beta,self).setQuadrature(maxOrder)
pts,wts = quads.j_roots(self.quadOrd,self.alpha,self.beta)
self.pts=self.convertToActual(pts)
for w,wt in enumerate(wts):
#TODO
wts[w]=wt/(2.*self.hrange)
self.wts=wts
def setQuadrature(self, maxOrder, verbose=False):
super(Beta, self).setQuadrature(maxOrder)
pts, wts = quads.j_roots(self.quadOrd, self.alpha, self.beta)
self.pts = self.convertToActual(pts)
for w, wt in enumerate(wts):
#TODO
wts[w] = wt / (2. * self.hrange)
self.wts = wts
def j_roots(n, alpha, beta, method='newton'):
"""
Returns the roots of the nth order Jacobi polynomial, P^(alpha,beta)_n(x)
and weights (w) to use in Gaussian Quadrature over [-1,1] with weighting
function (1-x)**alpha (1+x)**beta with alpha,beta > -1.
Parameters
----------
n : integer
number of roots
alpha,beta : scalars
defining shape of Jacobi polynomial
method : 'newton' or 'eigenvalue'
uses Newton Raphson to find zeros of the Hermite polynomial (Fast)
or eigenvalue of the jacobi matrix (Slow) to obtain the nodes and
weights, respectively.
Returns
-------
x : ndarray
roots
w : ndarray
weights
Examples
--------
>>> [x,w]= j_roots(10,0,0)
>>> sum(x*w)
2.7755575615628914e-16
See also
--------
qrule, gaussq
References
----------
[1] Golub, G. H. and Welsch, J. H. (1969)
'Calculation of Gaussian Quadrature Rules'
Mathematics of Computation, vol 23,page 221-230,
[2]. Stroud and Secrest (1966), 'gaussian quadrature formulas',
prentice-hall, Englewood cliffs, n.j.
"""
_assert((-1 < alpha) & (-1 < beta),
'alpha and beta must be greater than -1')
if not method.startswith('n'):
return ort.j_roots(n, alpha, beta)
return _j_roots_newton(n, alpha, beta)
def j_roots(n, alpha, beta, method='newton'):
'''
Returns the roots of the nth order Jacobi polynomial, P^(alpha,beta)_n(x)
and weights (w) to use in Gaussian Quadrature over [-1,1] with weighting
function (1-x)**alpha (1+x)**beta with alpha,beta > -1.
Parameters
----------
n : integer
number of roots
alpha,beta : scalars
defining shape of Jacobi polynomial
method : 'newton' or 'eigenvalue'
uses Newton Raphson to find zeros of the Hermite polynomial (Fast)
or eigenvalue of the jacobi matrix (Slow) to obtain the nodes and
weights, respectively.
Returns
-------
x : ndarray
roots
w : ndarray
weights
Example
--------
>>> [x,w]= j_roots(10,0,0)
>>> sum(x*w)
2.7755575615628914e-16
See also
--------
qrule, gaussq
Reference
---------
[1] Golub, G. H. and Welsch, J. H. (1969)
'Calculation of Gaussian Quadrature Rules'
Mathematics of Computation, vol 23,page 221-230,
[2]. Stroud and Secrest (1966), 'gaussian quadrature formulas',
prentice-hall, Englewood cliffs, n.j.
'''
if not method.startswith('n'):
return ort.j_roots(n, alpha, beta)
return _j_roots_newton(n, alpha, beta)
def initQuad(self, orderquad):
import scipy.special.orthogonal as op
self.level2(orderquad)
self.quadphi_over[:,0] = op.j_roots(self.orderquad,0,1)[0]
self.quadphi_over[:,1] = op.j_roots(self.orderquad,0,1)[1]
self.quadphi_under[:,0] = op.j_roots(self.orderquad,0,0)[0]
self.quadphi_under[:,1] = op.j_roots(self.orderquad,0,0)[1]
self.quadsingular[:,0] = op.j_roots(self.orderquad,2,-0.5)[0]
self.quadsingular[:,1] = map(lambda y: y,
op.j_roots(self.orderquad,2,-0.5)[1]/
map(lambda x: (1-x)**2*(1+x)**(-0.5),
op.j_roots(self.orderquad,2,-0.5)[0]))
def initQuad(self, orderquad):
import scipy.special.orthogonal as op
self.level2(orderquad)
self.quadphi_over[:, 0] = op.j_roots(self.orderquad, 0, 1)[0]
self.quadphi_over[:, 1] = op.j_roots(self.orderquad, 0, 1)[1]
self.quadphi_under[:, 0] = op.j_roots(self.orderquad, 0, 0)[0]
self.quadphi_under[:, 1] = op.j_roots(self.orderquad, 0, 0)[1]
self.quadsingular[:, 0] = op.j_roots(self.orderquad, 2, -0.5)[0]
self.quadsingular[:, 1] = map(
lambda y: y,
op.j_roots(self.orderquad, 2, -0.5)[1] /
map(lambda x: (1 - x)**2 * (1 + x)**(-0.5),
op.j_roots(self.orderquad, 2, -0.5)[0]))
def test_j_roots():
rf = lambda a, b: lambda n, mu: orth.j_roots(n, a, b, mu)
ef = lambda a, b: lambda n, x: orth.eval_jacobi(n, a, b, x)
wf = lambda a, b: lambda x: (1 - x)**a * (1 + x)**b
vgq = verify_gauss_quad
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1., 1., 5)
vgq(rf(-0.5, -0.75),
ef(-0.5, -0.75),
wf(-0.5, -0.75),
-1.,
1.,
25,
atol=1e-12)
vgq(rf(-0.5, -0.75),
ef(-0.5, -0.75),
wf(-0.5, -0.75),
-1.,
1.,
100,
atol=1e-11)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 5)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 25, atol=1.5e-13)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 100, atol=1e-12)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 5, atol=2e-13)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 25, atol=2e-13)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 100, atol=1e-12)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 5)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 25, atol=1e-13)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 100, atol=2e-13)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1., 5)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1., 25)
vgq(rf(18.24, 27.3),
ef(18.24, 27.3),
wf(18.24, 27.3),
-1.,
1.,
100,
atol=1e-13)
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1., 1., 5, atol=1e-13)
vgq(rf(47.1, -0.2),
ef(47.1, -0.2),
wf(47.1, -0.2),
-1.,
1.,
25,
atol=2e-13)
vgq(rf(47.1, -0.2),
ef(47.1, -0.2),
wf(47.1, -0.2),
-1.,
1.,
100,
atol=1e-11)
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1., 1., 5)
vgq(rf(2.25, 68.9),
ef(2.25, 68.9),
wf(2.25, 68.9),
-1.,
1.,
25,
atol=1e-13)
vgq(rf(2.25, 68.9),
ef(2.25, 68.9),
wf(2.25, 68.9),
-1.,
1.,
100,
atol=1e-13)
# when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
xj, wj = orth.j_roots(6, 0.0, 0.0)
xl, wl = orth.p_roots(6)
assert_allclose(xj, xl, 1e-14, 1e-14)
assert_allclose(wj, wl, 1e-14, 1e-14)
# when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
xj, wj = orth.j_roots(6, 4.0, 4.0)
xc, wc = orth.cg_roots(6, 4.5)
assert_allclose(xj, xc, 1e-14, 1e-14)
assert_allclose(wj, wc, 1e-14, 1e-14)
x, w = orth.j_roots(5, 2, 3, False)
y, v, m = orth.j_roots(5, 2, 3, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
muI, muI_err = integrate.quad(wf(2, 3), -1, 1)
assert_allclose(m, muI, rtol=muI_err)
assert_raises(ValueError, orth.j_roots, 0, 1, 1)
assert_raises(ValueError, orth.j_roots, 3.3, 1, 1)
assert_raises(ValueError, orth.j_roots, 3, -2, 1)
assert_raises(ValueError, orth.j_roots, 3, 1, -2)
assert_raises(ValueError, orth.j_roots, 3, -2, -2)
def j_roots(n, alpha, beta, method='newton'):
'''
Returns the roots (x) of the nth order Jacobi polynomial, P^(alpha,beta)_n(x)
and weights (w) to use in Gaussian Quadrature over [-1,1] with weighting
function (1-x)**alpha (1+x)**beta with alpha,beta > -1.
Parameters
----------
n : integer
number of roots
alpha,beta : scalars
defining shape of Jacobi polynomial
method : 'newton' or 'eigenvalue'
uses Newton Raphson to find zeros of the Hermite polynomial (Fast)
or eigenvalue of the jacobi matrix (Slow) to obtain the nodes and
weights, respectively.
Returns
-------
x : ndarray
roots
w : ndarray
weights
Example
--------
>>> [x,w]= j_roots(10,0,0)
>>> sum(x*w)
2.7755575615628914e-16
See also
--------
qrule, gaussq
Reference
---------
[1] Golub, G. H. and Welsch, J. H. (1969)
'Calculation of Gaussian Quadrature Rules'
Mathematics of Computation, vol 23,page 221-230,
[2]. Stroud and Secrest (1966), 'gaussian quadrature formulas',
prentice-hall, Englewood cliffs, n.j.
'''
if not method.startswith('n'):
[x, w] = ort.j_roots(n, alpha, beta)
else:
max_iter = 10
releps = 3e-14
# Initial approximations to the roots go into z.
alfbet = alpha + beta
z = np.cos(np.pi * (np.arange(1, n + 1) - 0.25 + 0.5 * alpha) / (n + 0.5 * (alfbet + 1)))
L = zeros((3, len(z)))
k0 = 0
kp1 = 1
for _its in xrange(max_iter):
#Newton's method carried out simultaneously on the roots.
tmp = 2 + alfbet
L[k0, :] = 1
L[kp1, :] = (alpha - beta + tmp * z) / 2
for j in xrange(2, n + 1):
#Loop up the recurrence relation to get the Jacobi
#polynomials evaluated at z.
km1 = k0
k0 = kp1
kp1 = np.mod(kp1 + 1, 3)
a = 2. * j * (j + alfbet) * tmp
tmp = tmp + 2
c = 2 * (j - 1 + alpha) * (j - 1 + beta) * tmp
b = (tmp - 1) * (alpha ** 2 - beta ** 2 + tmp * (tmp - 2) * z)
L[kp1, :] = (b * L[k0, :] - c * L[km1, :]) / a
#L now contains the desired Jacobi polynomials.
#We next compute pp, the derivatives with a standard
# relation involving the polynomials of one lower order.
pp = (n * (alpha - beta - tmp * z) * L[kp1, :] + 2 * (n + alpha) * (n + beta) * L[k0, :]) / (tmp * (1 - z ** 2))
dz = L[kp1, :] / pp
z = z - dz # Newton's formula.
if not any(abs(dz) > releps * abs(z)):
break
else:
warnings.warn('too many iterations in jrule')
x = z # %Store the root and the weight.
w = np.exp(sp.gammaln(alpha + n) + sp.gammaln(beta + n) - sp.gammaln(n + 1) -
sp.gammaln(alpha + beta + n + 1)) * tmp * 2 ** alfbet / (pp * L[k0, :])
return x, w
def j_roots(n, alpha, beta, method='newton'):
'''
Returns the roots of the nth order Jacobi polynomial, P^(alpha,beta)_n(x)
and weights (w) to use in Gaussian Quadrature over [-1,1] with weighting
function (1-x)**alpha (1+x)**beta with alpha,beta > -1.
Parameters
----------
n : integer
number of roots
alpha,beta : scalars
defining shape of Jacobi polynomial
method : 'newton' or 'eigenvalue'
uses Newton Raphson to find zeros of the Hermite polynomial (Fast)
or eigenvalue of the jacobi matrix (Slow) to obtain the nodes and
weights, respectively.
Returns
-------
x : ndarray
roots
w : ndarray
weights
Example
--------
>>> [x,w]= j_roots(10,0,0)
>>> sum(x*w)
2.7755575615628914e-16
See also
--------
qrule, gaussq
Reference
---------
[1] Golub, G. H. and Welsch, J. H. (1969)
'Calculation of Gaussian Quadrature Rules'
Mathematics of Computation, vol 23,page 221-230,
[2]. Stroud and Secrest (1966), 'gaussian quadrature formulas',
prentice-hall, Englewood cliffs, n.j.
'''
if not method.startswith('n'):
[x, w] = ort.j_roots(n, alpha, beta)
else:
max_iter = 10
releps = 3e-14
# Initial approximations to the roots go into z.
alfbet = alpha + beta
z = np.cos(np.pi * (np.arange(1, n + 1) - 0.25 + 0.5 * alpha) /
(n + 0.5 * (alfbet + 1)))
L = zeros((3, len(z)))
k0 = 0
kp1 = 1
for _its in xrange(max_iter):
# Newton's method carried out simultaneously on the roots.
tmp = 2 + alfbet
L[k0, :] = 1
L[kp1, :] = (alpha - beta + tmp * z) / 2
for j in xrange(2, n + 1):
# Loop up the recurrence relation to get the Jacobi
# polynomials evaluated at z.
km1 = k0
k0 = kp1
kp1 = np.mod(kp1 + 1, 3)
a = 2. * j * (j + alfbet) * tmp
tmp = tmp + 2
c = 2 * (j - 1 + alpha) * (j - 1 + beta) * tmp
b = (tmp - 1) * (alpha ** 2 - beta ** 2 + tmp * (tmp - 2) * z)
L[kp1, :] = (b * L[k0, :] - c * L[km1, :]) / a
# L now contains the desired Jacobi polynomials.
# We next compute pp, the derivatives with a standard
# relation involving the polynomials of one lower order.
pp = ((n * (alpha - beta - tmp * z) * L[kp1, :] +
2 * (n + alpha) * (n + beta) * L[k0, :]) /
(tmp * (1 - z ** 2)))
dz = L[kp1, :] / pp
z = z - dz # Newton's formula.
if not any(abs(dz) > releps * abs(z)):
break
else:
warnings.warn('too many iterations in jrule')
x = z # %Store the root and the weight.
f = (sp.gammaln(alpha + n) + sp.gammaln(beta + n) -
sp.gammaln(n + 1) - sp.gammaln(alpha + beta + n + 1))
w = (np.exp(f) * tmp * 2 ** alfbet / (pp * L[k0, :]))
return x, w