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Scilab manual >> UMFPACK Interface > cond2sp


computes an approximation of the 2-norm condition number of a s.p.d. sparse matrix

Calling Sequence

[K2, lm, vm, lM, vM] = cond2sp(A, C_ptr [, rtol, itermax, verb])



a real symmetric positive definite sparse matrix


a pointer to a Cholesky factorization (got with taucs_chfact)


(optional) relative tolerance (default 1.e-3) (see details in DESCRIPTION)


(optional) maximum number of iterations in the underlying algorithms (default 30)


(optional) boolean, must be %t for displaying the intermediary results, and %f (default) if you do not want.


estimated 2-norm condition number K2 = ||A||_2 ||A^(-1)||_2 = lM/lm


(real positive scalar) minimum eigenvalue


associated eigenvector


(real positive scalar) maximum eigenvalue


associated eigenvector


This quick and dirty function computes (lM,vM) using the iterative power method and (lm,vm) with the inverse iterative power method, then K2 = lM/lm. For each method the iterations are stopped until the following condition is met :

| (l_new - l_old)/l_new | < rtol

but 4 iterations are nevertheless required and also the iterations are stopped if itermax is reached (and a warning message is issued). As the matrix is symmetric this is the rayleigh quotient which gives the estimated eigenvalue at each step (lambda = v'*A*v). You may called this function with named parameter, for instance if you want to see the intermediary result without setting yourself the rtol and itermax parameters you may called this function with the syntax :

[K2, lm, vm, lM, vM] = cond2sp(A , C_ptr, verb=%t )


Currently there is no verification for the input parameters !


This function is intended to get an approximation of the 2-norm condition number (K2) and with the methods used, the precision on the obtained eigenvectors (vM and vm) are generally not very good. If you look for a smaller residual ||Av - l*v||, you may apply some inverse power iterations from v0 with the matrix :

B = A - l0*speye(A)

For instance, applied 5 such iterations for (lm,vm) is done with :

l0 = lm ; v0 = vm;  // or l0 = lM ; v0 = vM;  // to polish (lM,vM)
B = A - l0*speye(A);
LUp = umf_lufact(B);
vr = v0; nstep = 5;
for i=1:nstep, vr = umf_lusolve(LUp, vr, "Ax=b", B); vr = vr/norm(vr) ; end
umf_ludel(LUp); // if you do not use anymore this factorization
lr = vr'*A*vr;
norm_r0 = norm(A*v0 - l0*v0);
norm_rr = norm(A*vr - lr*vr);
// Bauer-Fike error bound...
mprintf(" first estimated eigenvalue : l0 = %e \n\t", l0) 
mprintf(" |l-l0| <= ||Av0-l0v0|| = %e , |l-l0|/l0 <= %e \n\r", norm_r0, norm_r0/l0)
mprintf(" raffined estimated eigenvalue : lr = %e \n\t", lr) 
mprintf(" |l-lr| <= ||Avr-lrvr|| = %e , |l-lr|/lr <= %e \n\r", norm_rr, norm_rr/lr)


[A] = ReadHBSparse(SCI+"/modules/umfpack/examples/bcsstk24.rsa");
C_ptr = taucs_chfact(A);
[K2, lm, vm, lM, vM] = cond2sp(A , C_ptr, 1.e-5, 50, %t );


Bruno Pincon <>

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Last updated:
Wed Jan 26 16:25:10 CET 2011