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Mark Velednitsky

Publications and source records attributed to Mark Velednitsky.

3 recordsLinked to original sources

Solving $(k-1)$-Stable Instances of k-Terminal Cut with Isolating Cuts

The k-Terminal Cut problem, also known as the Multiway Cut problem, is defined on an edge-weighted graph with $k$ distinct vertices called "terminals." The goal is to remove a minimum weight collection of edges from the graph such that there is no path between any pair of terminals. The problem is NP-hard. Isolating cuts are minimum cuts that separate one terminal from the rest. The union of all the isolating cuts, except the largest, is a $(2-2/k)$-approximation to the optimal k-Terminal Cut. This is the only currently-known approximation algorithm for k-Terminal Cut which does not require solving a linear program. An instance of k-Terminal Cut is $γ$-stable if edges in the cut can be multiplied by up to $γ$ without changing the unique optimal solution. In this paper, we show that, in any $(k-1)$-stable instance of k-Terminal Cut, the source sets of the isolating cuts are the source sets of the unique optimal solution of that k-Terminal Cut instance. We conclude that the $(2-2/k)$-approximation algorithm returns the optimal solution on $(k-1)$-stable instances. Ours is the first result showing that this $(2-2/k)$-approximation is an exact optimization algorithm on a special class of graphs. We also show that our $(k-1)$-stability result is tight. We construct $(k-1-ε)$-stable instances of the k-Terminal Cut problem which only have trivial isolating cuts: that is, the source set of the isolating cuts for each terminal is just the terminal itself. Thus, the $(2-2/k)$-approximation does not return an optimal solution.

cs.DS

DISPATCH: An Optimally-Competitive Algorithm for Maximum Online Perfect Bipartite Matching with i.i.d. Arrivals

This work presents an optimally-competitive algorithm for the problem of maximum weighted online perfect bipartite matching with i.i.d. arrivals. In this problem, we are given a known set of workers, a distribution over job types, and non-negative utility weights for each pair of worker and job types. At each time step, a job is drawn i.i.d. from the distribution over job types. Upon arrival, the job must be irrevocably assigned to a worker and cannot be dropped. The goal is to maximize the expected sum of utilities after all jobs are assigned. We introduce DISPATCH, a 0.5-competitive, randomized algorithm. We also prove that 0.5-competitive is the best possible. DISPATCH first selects a "preferred worker" and assigns the job to this worker if it is available. The preferred worker is determined based on an optimal solution to a fractional transportation problem. If the preferred worker is not available, DISPATCH randomly selects a worker from the available workers. We show that DISPATCH maintains a uniform distribution over the workers even when the distribution over the job types is non-uniform.

cs.DS