In this letter, we consider the incorrigible sets of binary linear codes. First, we show that the incorrigible set enumerator of a binary linear code is tantamount to the Tutte polynomial of the vector matroid induced by the parity-check matrix of the code. A direct consequence is that determining the incorrigible set enumerator of binary linear codes is #P-hard. Then for a cycle code, we express its incorrigible set enumerator via the Tutte polynomial of the graph describing the code. Furthermore, we provide the explicit formula of incorrigible set enumerators of cycle codes constructed from complete graphs.

While the graph minor theorem by Robertson and Seymour assures that any minor-closed class of graphs can be characterized by a finite list of excluded minors, such a succinct characterization by excluded minors is not always possible in matroids which are combinatorial abstraction from graphs. The class of matroids representable over a given infinite field is known to have an infinite number of excluded minors. In this paper, we show that, for any algebraic element x over the rational field ℚ the degree of whose minimal polynomial is 2, there exist infinitely many ℚ[x]-representable excluded minors of rank 3 for ℚ-representability. This implies that the knowledge that a given matroid is F-representable where F is a larger field than ℚ does not decrease the difficulty of excluded minors' characterization of ℚ-representability.

We investigate excluded minor characterizations of two fundamental classes of matroids: orientable matroids and representable matroids. We prove (i) for any fixed field F, there exist infinitely many excluded minors of rank 3 for the union of the class of orientable matroids and the class of F-representable matroids, and (ii) for any fixed field F with characteristic 0, there exist infinitely many orientable excluded minors of rank 3 for intersection of the class of orientable matroids and the class of F-representable matroids. We show these statements by explicitly constructing infinite families of excluded minors.

Irreversible k-conversion set is introduced in connection with the mathematical modeling of the spread of diseases or opinions. We show that the problem to find a minimum irreversible 2-conversion set can be solved in O(n2log 6n) time for graphs with maximum degree at most 3 (subcubic graphs) by reducing it to the graphic matroid parity problem, where n is the number of vertices in a graph. This affirmatively settles an open question posed by Kyncl et al. (2014).

In this paper, we further study linear network error correction code on a multicast network and attempt to establish a connection between linear network error correction codes and representable matroids. We propose a similar but more accurate definition of matroidal error correction network which has been introduced by K. Prasad et al. Moreover, we extend this concept to a more general situation when the given linear network error correction codes have different error correcting capacity at different sinks. More importantly, using a different method, we show that a multicast error correction network is scalar-linearly solvable if and only if it is a matroidal error correction network.

The concepts of M-convexity and L-convexity, introduced by Murota (1996, 1998) for functions on the integer lattice, extract combinatorial structures in well-solved nonlinear combinatorial optimization problems. These concepts are extended to polyhedral convex functions and quadratic functions on the real space by Murota-Shioura (2000, 2001). In this paper, we consider a further extension to general convex functions. The main aim of this paper is to provide rigorous proofs for fundamental properties of general M-convex and L-convex functions.

M-convex functions have various desirable properties as convexity in discrete optimization. We can find a global minimum of an M-convex function by a greedy algorithm, i.e., so-called descent algorithms work for the minimization. In this paper, we apply a scaling technique to a greedy algorithm and propose an efficient algorithm for the minimization of an M-convex function. Computational results are also reported.

This note investigates the characterizing properties of the level sets of an M-convex function introduced by Murota.

The linear complementarity problem (LCP) is one of the most widely studied mathematical programming problems. The theory of LCP can be extended to oriented matroids which are combinatorial abstractions of linear subspaces of Euclidean spaces. This paper briefly surveys the LCP, oriented matroids and algorithms for the LCP on oriented matroids.

Given a combinatorial problem on a set of weighted elements, if we change the weight using a parameter, we obtain a parametric version of the problem, which is often used as a tool for solving mathematical programming problems. One interesting question is how to describe and analyze the trajectory of the solution. If we consider the trajectory of each weight function as a curve in a plane, we have a set of curves from the problem instance. The curves induces a cell complex called an arrangement, which is a popular research target in computational geometry. Especially, for the parametric version of the problem of computing the minimum weight base of a matroid or polymatroid, the trajectory of the solution becomes a subcomplex in an arrangement. We introduce the interaction between the two research areas, combinatorial optimization and computational geometry, through this bridge.

The invariant polynomials of discrete systems such as graphs, matroids, hyperplane arrangements, and simplicial complexes, have been theoretically investigated actively in recent years. These invariants include the Tutte polynomial of a graph and a matroid, the chromatic polynomial of a graph, the network reliability of a network, the Jones polynomial of a link, the percolation function of a grid, etc. The computational complexity issues of computing these invariants have been studied and most of them are shown to be #P-complete. But, these complexity results do not imply that we cannot compute the invariants of a given instance of moderate size in practice. To meet large demand of computing these invariants in practice, there have been proposed a framework of computing the invariants by using the binary decision diagrams (BDD for short). This provides mildly exponential algorithms which are useful to solve moderate-size practical problems. This paper surveys the BDD-based approach to computing the invariants, together with some computational results showing the usefulness of the framework.

This is a survey of algorithmic results in the theory of "discrete convex analysis" for integer-valued functions defined on integer lattice points. The theory parallels the ordinary convex analysis, covering discrete analogues of the fundamental concepts such as conjugacy, the Fenchel min-max duality, and separation theorems. The technical development is based on matroid-theoretic concepts, in particular, submodular functions and exchange axioms.