Multitasking Capacity: Hardness Results and Improved Constructions
Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi,, Daniel Reichman, Igor Shinkar, Tal Wagner, Alexander Yu

TL;DR
This paper investigates the computational hardness of determining the maximal induced matching ratio in bipartite graphs, introduces bipartite half-covers, and constructs graphs with near-optimal multitasking capacity, advancing understanding of these complex problems.
Contribution
It establishes new hardness results for computing multitasking capacity and connected matchings, introduces bipartite half-covers, and constructs graphs with near-optimal induced matching ratios.
Findings
Proves nearly optimal hardness of approximation for connected matching in bipartite graphs.
Introduces bipartite half-covers as a new combinatorial object.
Constructs bipartite graphs with high multitasking capacity close to the theoretical maximum.
Abstract
We consider the problem of determining the maximal such that every matching of size (or at most ) in a bipartite graph contains an induced matching of size at least . This measure was recently introduced in Alon et al. (NIPS 2018) and is motivated by connectionist models of cognition as well as modeling interference in wireless and communication networks. We prove various hardness results for computing either exactly or approximately. En route to our results, we also consider the maximum connected matching problem: determining the largest matching in a graph such that every two edges in are connected by an edge. We prove a nearly optimal hardness of approximation result (under randomized reductions) for connected matching in bipartite graphs (with both sides of cardinality ). Towards this end we…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Optimization and Search Problems · Advanced Graph Theory Research
