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We are given a stable matching instance $A$ and a set $S$ of errors that can be introduced into $A$. Each error consists of applying a specific permutation to the preference list of a chosen boy or a chosen girl. Assume that $A$ is being transmitted over a channel which introduces one error from set $S$; as a result, the channel outputs this new instance. We wish to find a matching that is stable for $A$ and for each of the $|S|$ possible new instances. If $S$ is picked from a special class of errors, we give an $O(|S| p(n))$ time algorithm for this problem. We call the obtained matching a fully robust stable matching w.r.t. $S$. In particular, if $S$ is polynomial sized, then our algorithm runs in polynomial time. Our algorithm is based on new, non-trivial structural properties of the lattice of stable matchings; these properties pertain to certain join semi-sublattices of the lattice. Birkhoffs Representation Theorem for finite distributive lattices plays a special role in our algorithms.
Understanding structural controllability of a complex network requires to identify a Minimum Input nodes Set (MIS) of the network. It has been suggested that finding an MIS is equivalent to computing a maximum matching of the network, where the unmat
We consider networks of small, autonomous devices that communicate with each other wirelessly. Minimizing energy usage is an important consideration in designing algorithms for such networks, as battery life is a crucial and limited resource. Working
We provide necessary and sufficient conditions on the preferences of market participants for a unique stable matching in models of two-sided matching with non-transferable utility. We use the process of iterated deletion of unattractive alternatives
For a set A of n applicants and a set I of m items, we consider a problem of computing a matching of applicants to items, i.e., a function M mapping A to I; here we assume that each applicant $x in A$ provides a preference list on items in I. We say
For a set $A$ of $n$ people and a set $B$ of $m$ items, with each person having a preference list that ranks all items from most wanted to least wanted, we consider the problem of matching every person with a unique item. A matching $M$ is called $ep