def covsel_cvx_dense(R, U, rows, cols, num_iterations=500, min_ev=1e-2):
min_ei = smallest_ev_arpack(R, U, rows, cols)
print "min_ei is %f" % min_ei
if min_ei < min_ev:
R0 = R - min_ei + min_ev
else:
R0 = R
return run_cvx_dense(R0, U, rows, cols, num_iterations)
def covsel_cvx_cholmod(
R, U, rows, cols, k, psd_tolerance=1e-6, factor=None, num_iterations=500, finish_early=True, debug=True
):
if debug:
tic("smallest ev", "covsel_cvx_cholmod")
min_ei = smallest_ev_arpack(R, U, rows, cols)
if debug:
tic("min_ei is %f" % min_ei, "covsel_cvx_cholmod")
if min_ei < 0:
R0 = R - min_ei + 1e-3
else:
R0 = R
return run_cvx_cholmod(R0, U, rows, cols, k, psd_tolerance, factor, num_iterations, finish_early)
def covsel_quick(R,U,rows,cols,lbda=1e6):
min_ei = smallest_ev_arpack(R, U, rows, cols)
if min_ei < 0:
print "min_ei is %f"%min_ei
R0 = R - min_ei + 1e-3
else:
R0 = R
n = len(R)
m = len(U)
S = build_dense(R0, U, rows, cols)
L=lbda * np.ones((n,n),dtype=np.double)
L[np.arange(n),np.arange(n)] = 0
L[rows,cols]=0
L[cols,rows]=0
(X_, W_, opt, time) = run_quic(S, L)
D = X_.diagonal()
P = X_[rows,cols]
R2 = W_.diagonal()
U2 = W_[rows,cols]
print "Quic: Error is %f diag, %f outer"%(linalg.norm(R0-R2),linalg.norm(U-U2))
return (D,P)