A parameterized view on matroid optimization problems

Matroid theory gives us powerful techniques for understanding combinatorial optimization problems and for designing polynomial-time algorithms. However, several natural matroid problems, such as 3-matroid intersection, are NP-hard. Here we investigate these problems from the parameterized complexity point of view: instead of the trivial n^O(k)$n^{O\left(k\right)}$ time brute force algorithm for finding a k$k$-element solution, we try to give algorithms with uniformly polynomial (i.e., f(k)⋅n^O(1)$f\left(k\right)\cdot n^{O\left(1\right)}$) running time. The main result is that if the ground set of a represented linear matroid is partitioned into blocks of size ?$?$, then we can determine in randomized time f(k,?)⋅n^O(1)$f(k,?)\cdot n^{O\left(1\right)}$ whether there is an independent set that is the union of k$k$ blocks. As a consequence, algorithms with similar running time are obtained for other problems such as finding a k$k$-element set in the intersection of ?$?$ matroids, or finding k$k$ terminals in a network such that each of them can be connected simultaneously to the source by ?$?$ disjoint paths.