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Matroid Bases with Cardinality Constraints on the Intersection Matroid Bases with Cardinality Constraints on the Intersection Stefan Lendl∗1, Britta Peis†2, and Veerle Timmermans‡2 1Graz University of Technology, Institute of Discrete Mathematics 2RWTH Aachen, Chair of Management Science Given two matroids M1 = (E, B1) and M2 = (E, B2) on a common ground set E with base sets B1 and B2, some integer k ∈ N, and two cost functions c1, c2 : E → R, we consider the optimization problem to find a basis X ∈B1 and a basis Y ∈B2 minimizing cost e∈X c1(e)+ e∈Y c2(e) subject to either a lower bound constraint |X ∩Y |≤ k, an upper bound constraint |X ∩ Y |≥ k, or an equality constraint |X ∩ Y | = k on the size P P of the intersection of the two bases X and Y . The problem with lower bound constraint turns out to be a generalization of the Recoverable Robust Matroid problem under interval uncertainty representation for which the question for a strongly polynomial- time algorithm was left as an open question in [7]. We show that the two problems with lower and upper bound constraints on the size of the intersection can be reduced to weighted matroid intersection, and thus be solved with a strongly polynomial-time primal-dual algorithm. The question whether the problem with equality constraint can also be solved efficiently turned out to be a lot harder. As our main result, we present a strongly polynomial, primal-dual algorithm for the problem with equality constraint on the size of the intersection. Additionally, we discuss generalizations of the problems from matroids to polyma- troids, and from two to three or more matroids. arXiv:1907.04741v2 [math.OC] 6 Dec 2019 1. Introduction Matroids are fundamental and well-studied structures in combinatorial optimization. Recall that a matroid M is a tuple M = (E, F), consisting of a finite ground set E and a family of subsets F ⊆ 2E, called the independent sets, satisfying (i) ∅ ∈ F, (ii) if F ∈ F and F ′ ⊂ F , then F ′ ∈ F, and (iii) if F, F ′ ∈ F with |F ′| > |F |, then there exists some element e ∈ F ′ \ F satisfying F ∪ {e} ∈ F. As usual when dealing with matroids, we assume that a matroid is specified via an independence oracle, that, given S ⊆ E as input, checks whether or not S ∈ F. Any inclusion-wise maximal set in independence system F is called a basis of F. Note that the set of bases B = B(M) of a matroid M ∗[email protected] †[email protected] ‡[email protected] 1 uniquely defines its independence system via F(B) = {F ⊆ E | F ⊆ B for some B ∈ B}. Because of their rich structure, matroids allow for various different characterizations (see, e.g., [12]). In particular, matroids can be characterized algorithmically as the only downward-closed structures for which a simple greedy algorithm is guaranteed to return a basis B ∈ B of minimum cost R c(B) = e∈B c(e) for any linear cost function c : E → +. Moreover, the problem to find a min-cost common base in two matroids M = (E, B ) and M = (E, B ) on the same ground P 1 1 2 2 set, or the problem to maximize a linear function over the intersection F1 ∩ F2 of two matroids M1 = (E, F1) and M2 = (E, F2) can be done efficiently with a strongly-polynomial primal-dual algorithm (cf. [4]). Optimization over the intersection of three matroids, however, is easily seen to be NP-hard. See [11] for most recent progress on approximation results for the latter problem. Optimization on matroids, their generalization to polymatroids, or on the intersection of two (poly-)matroids, capture a wide range of interesting problems. In this paper, we introduce and study yet another variant of matroid-optimization problems. Our problems can be seen as a variant or generalization of matroid intersection: we aim at minimizing the sum of two linear cost functions over two bases chosen from two matroids on the same ground set with an additional cardinality constraint on the intersection of the two bases. As it turns out, the problems with lower and upper bound constraint are computationally equivalent to matroid intersection, while the problem with equality constraint seems to be strictly more general, and to lie somehow on the boundary of efficiently solvable combinatorial optimization problems. Interestingly, while the problems on matroids with lower, upper, or equality constraint on the intersection can be shown to be solvable in strongly polynomial time, the extension of the problems towards polymatroids is solvable in strongly polynomial time for the lower bound constraint, but NP-hard for both, upper and equality constraints. The model Given two matroids M1 = (E, B1) and M2 = (E, B2) on a common ground set E with base sets B1 and B2, some integer k ∈ N, and two cost functions c1, c2 : E → R, we consider the optimization problem to find a basis X ∈ B1 and a basis Y ∈ B2 minimizing c1(X)+ c2(Y ) subject to either a lower bound constraint |X ∩ Y | ≥ k, an upper bound constraint |X ∩ Y | ≤ k, or an equality constraint |X ∩ Y | = k on the size of the intersection of the two bases X and Y . Here, as usual, we write c1(X)= e∈X c1(e) and c2(Y )= e∈Y c2(e) to shorten notation. Let us denote the following problem by (P k). P= P min c1(X)+ c2(Y ) s.t. X ∈B1 Y ∈B2 |X ∩ Y | = k Accordingly, if |X ∩ Y | = k is replaced by either the upper bound constraint |X ∩ Y |≤ k, or the lower bound constraint |X ∩ Y |≥ k, the problem is called (P≤k) or (P≥k), respectively. Clearly, it only makes sense to consider integers k in the range between 0 and K := min{rk(M1), rk(M2)}, where rk(Mi) for i ∈ {1, 2} is the rank of matroid Mi, i.e., the cardinality of each basis in Mi which is unique due to the augmentation property (iii). For details on matroids, we refer to [12]. Related Literature on the Recoverable Robust Matroid problem: Problem (P≥k) in the special case where B1 = B2 is known and well-studied under the name Recoverable Robust Matroid Problem Under Interval Uncertainty Representation, see [1,7,8] and Section 4 below. For this special case of (P≥k), B¨using [1] presented an algorithm which is exponential in k. In 2017, Hradovich, Kaperski, 2 and Zieli´nski [8] proved that the problem can be solved in polynomial time via some iterative relaxation algorithm and asked for a strongly polynomial time algorithm. Shortly after, the same authors presented in [7] a strongly polynomial time primal-dual algorithm for the special case of the problem on a graphical matroid. The question whether a strongly polynomial time algorithm for (P≥k) with B1 = B2 exists remained open. Our contribution. In Section 2, we show that both, (P≤k) and (P≥k), can be polynomially reduced to weighted matroid intersection. Since weighted matroid intersection can be solved in strongly polynomial time by some very elegant primal-dual algorithm [4], this answers the open question raised in [8] affirmatively. As we can solve (P≤k) and (P≥k) in strongly polynomial time via some combinatorial algorithm, the question arises whether or not the problem with equality constraint (P=k) can be solved in strongly polynomial time as well. The answer to this question turned out to be more tricky than expected. As our main result, in Section 3, we provide a strongly polynomial, primal-dual algorithm that constructs an optimal solution for (P=k). The same algorithm can also be used to solve an extension, called (P ∆), of the Recoverable Robust Matroid problem under Interval Uncertainty Represenation, see Section 4. Then, in Section 5, we consider the generalization of problems (P≤k), (P≥k), and (P=k) from matroids to polymatroids with lower, upper, or equality bound constraint, respectively, on the size of the meet |x ∧ y| := e∈E min{xe,ye}. Interestingly, as it turns out, the generalization of (P≥k) can be solved in strongly polynomial time via reduction to some polymatroidal flow problem, while P the generalizations of (P≤k) and (P=k) can be shown to be weakly NP-hard, already for uniform polymatroids. Finally, in Section 6 we discuss the generalization of our matroid problems from two to three or more matroids. That is, we consider n matroids Mi = (E, Bi), i ∈ [n], and n linear cost functions ci : E → R, for i ∈ [n]. The task is to find n bases Xi ∈ Bi, i ∈ [n], minimizing the n n cost i=1 ci(Xi) subject to a cardinality constraint on the size of intersection | i=1 Xi|. When we are given an upper bound on the size of this intersection we can find an optimal solution within P T polynomial time. Though, when we have an equality or a lower bound constraint on the size of the intersection, the problem becomes strongly NP-hard. 2. Reduction of (P≤k) and (P≥k) to weighted matroid intersection We first note that (P≤k) and (P≥k) are computationally equivalent. To see this, consider any instance (M1, M2,k,c1, c2) of (P≥k), where M1 = (E, B1), and M2 = (E, B2) are two matroids on ∗ ∗ the same ground set E with base sets B1 and B2, respectively. Define c2 = −c2, k = rk(M1) − k, ∗ ∗ ∗ and let M2 = (E, B2) with B2 = {E \ Y | Y ∈B2} be the dual matroid of M2. Since ∗ (i) |X ∩ Y |≤ k ⇐⇒ |X ∩ (E \ Y )| = |X| − |X ∩ Y |≥ rk(M1) − k = k , and ∗ (ii) c1(X)+ c2(Y )= c1(X)+ c2(E) − c2(E \ Y )= c1(X)+ c2(E \ Y )+ c2(E), where c2(E) is a constant, it follows that (X,Y ) is a minimizer of (P≥k) for the given instance ∗ ∗ ∗ (M1, M2,k,c1, c2) if and only if (X, E \ Y ) is a minimizer of (P≤k∗ ) for (M1, M2, k , c1, c2).
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