# Computing the Spark: Mixed-Integer Programming for the (Vector) Matroid Girth Problem

We investigate the NP-hard problem of computing the spark of a matrix (i.e., the smallest number of linearly dependent columns), a key parameter in compressed sensing and sparse signal recovery. To that end, we identify polynomially solvable special cases, gather upper and lower bounding procedures, and propose several exact (mixed-)integer programming models and linear programming heuristics. In particular, we develop a branch-and-cut scheme to determine the girth of a matroid, focussing on the vector matroid case, for which the girth is precisely the spark of the representation matrix. Extensive numerical experiments demonstrate the effectiveness of our specialized algorithms compared to general-purpose black-box solvers applied to several mixed-integer programming models.

Code and test instances available per request; will become directly available on this page in the near future.

@article{Tillmann2019,

author = {Andreas M. Tillmann},

title = {{Computing the Spark: Mixed-Integer Programming\\for the (Vector) Matroid Girth Problem}},

journal = {{Computational Optimization and Applications}},

volume = {to appear},

year = {2019}

}