We propose a new self-stabilizing 1-maximal matching algorithm which is silent and works for any anonymous networks without a cycle of length of a multiple of 3 under a central unfair daemon. The 1-maximal matching is a 2 3 -approximation to the maximum matching, and expected to get more matching pairs than a maximal matching, which only guarantees a 1 2 -approximation. The time complexity of t...

We present a collection of new structural, algorithmic, and complexity results for two types of matching problems. The first problem involves the computation of k-maximal matchings, where a matching is k-maximal if it admits no augmenting path with ≤ 2k vertices. The second involves finding a maximal set of vertices that is matchable — comprising one side of the edges in some matching. Among ou...

A maximal matching of a graph is the set of edges such that the addition of an edge to this set violates the property of matching (i.e., no two edges of the matching share a vertex). We use the notion of assortative index (ranges from -1 to 1) to evaluate the extent of similarity of the end vertices constituting the edges of a matching. A maximal matching of the edges whose assortative index is...

We present a derandomization process which leads to efficient parallel algorithms for the maximal independent set and maximal matching problems. The derandomization of the general pairs PROFIT/COST problem depends on the derandomization of the bit pairs PROFIT/COST problem which follows Luby’s approach of using an O(n) sized sample space. This simplifies the approach presented in [16].

Given a collection of matchings M = (M1,M2, . . . ,Mq) (repetitions allowed), a matching M contained in ⋃ M is said to be s-rainbow for M if it contains representatives from s matchings Mi (where each edge is allowed to represent just one Mi). Formally, this means that there is a function φ : M → [q] such that e ∈ Mφ(e) for all e ∈ M , and |Im(φ)| > s. Let f(r, s, t) be the maximal k for which ...