Re: [Help-glpk] GLPK failure to invert matrix

[Andrew Makhorin]

> I have a problem that has few integer parameters defining the size
> of the problem. It's like the large non-square underdefined linear
> equation AX=F with extra >= conditions.

> When I downscale parameters such that problem becomes just the
> square linear equation GLPK fails with error beginning with size
> ~700:

>      0:   objval =   1.427134885e-02   infeas =   1.000000000e+00 (0)
>     200:   objval =   3.448379584e-03   infeas =   3.724860891e-01 (0)
>     203:   objval =  -7.866243389e-19   infeas =   1.314442221e-16 (500)
> *   203:   objval =  -7.866243389e-19   infeas =   8.259452150e-16 (500)
> *   400:   objval =   0.000000000e+00   infeas =   0.000000000e+00 (303)
> spx_invert: the basis matrix is ill-conditioned
> spx_simplex: numerical problems with basis matrix
> spx_simplex: sorry, basis recovery procedure not implemented yet
> lpx_simplex: cannot recover undefined or non-optimal solution
> Time used:   26.0 secs
> Memory used: 104.3M

This a lack of the glpk simplex solver. I hope to implement a new
version of the solver, which includes the basis recovery procedure.

> This suggests that GLPK failed to compute LU-factorization of the A
> matrix. At the same time LAPACK package is able to find
> LU-factorization and invert the same matrix quite easily with their
> 'dgetrf' and 'dgetri' functions called in sequence.

> Is it reasonable to expect GLPK to invert this kind of matrices?
> Would this be a valid enhancement request?

Ill-conditioned matrix means that its condition number is too large
(in this case is larger than 1e12), so the solution cannot be obtained
with a sufficient accuracy. AFAIK, dgetrf checks only for exact
singularity, besides it uses partial pivoting. Could you estimate the
condition number of your matrix?

>  I can provide this problem's description if necessary.

Please do that.


Andrew Makhorin