Optimal Matroid Bases with Intersection Constraints: Valuated Matroids, M-convex Functions, and Their Applications

Abstract

For two matroids and with the same ground set V and two cost functions and on, we consider the problem of finding bases of and of minimizing subject to a certain cardinality constraint on their intersection. Lendl, Peis, and Timmermans (2019) discussed modular cost functions: They reduced the problem to weighted matroid intersection for the case where the cardinality constraint is or; and designed a new primal-dual algorithm for the case where. The aim of this paper is to generalize the problems to have nonlinear convex cost functions, and to comprehend them from the viewpoint of discrete convex analysis. We prove that each generalized problem can be solved via valuated independent assignment, valuated matroid intersection, or-convex submodular flow, to offer a comprehensive understanding of weighted matroid intersection with intersection constraints. We also show the NP-hardness of some variants of these problems, which clarifies the coverage of discrete convex analysis for those problems. Finally, we present applications of our generalized problems in matroid congestion games and combinatorial optimization problems with interaction costs.