from jacobi_iteration import jacobi_eigensolver from davidson import davidsolver from jacobi_iteration import jacobi_rot from JOCC import JOCC import timeit ''' This script is old and should be phased out. Any testing should be done as a separeate script in each method file. ''' k = 4 # Matrix size TOL = 1.e-1 # Margin of error D = 100 # Size of diagonal shape N = 5000 # Number of itterations for Jacobi A = matrixgen(k, D) # e, w = powersolve(A, TOL) # Pow = np.sort(e) # print "Cumputed eigenvalues using Power" # print Pow # D = jacobi_eigensolver(np.asmatrix(A), N) # Jac = np.sort(np.diag(D)) # print "Computed eigenvalues using Jacobi" # print Jac eig, vec = np.linalg.eig(A) Eig = np.sort(eig)
def JOCC(Ain,N):
A=np.asarray(Ain)
alpha=A[0,0]
b=A[1:,0]
c=A[0,1:]
F=A[1:,1:]
guess=np.random.random(len(A)-1)
I=np.eye(len(A)-1)
z=guess
for i in range(N):
theta=alpha+np.dot(np.transpose(c),z)
z=jacobi_solver((F-theta*I),(-b),N)
return theta, z, guess
'''
It could probaby be modified to return the eigenvector approximation as well,
I think it's z'''
dim=50
diag_dom=50
iterations=100
Q=matrixgen(dim,diag_dom)
Q[0,0]=Q[0,0]+25
eigp=np.linalg.eigvals(Q)
eig, vec, guess = JOCC(Q,iterations)
print "Eig:", eig
print "python",eigp
