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Budget-constrained cut problems
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The minimum and maximum cuts of an undirected edge-weighted graph are classic problems in graph theory. While the Min-Cut Problem can be solved in P, the Max-Cut Problem is NP-Complete. Exact and heuristic methods have been developed for solving them. For both problems, we introduce a natural extension in which cutting an edge induces a cost. Our goal is to find a cut that minimizes the sum of the cut weights but, at the same time, restricts its total cut cost to a given budget. We prove that both restricted problems are NPComplete and we also study some of its properties. Finally, we develop exact algorithms to solve both as well as a non-exact algorithm for the min-cut case based on a Lagreangean relaxation that generally provides optimal solutions. Their performance is reported by an extensive computational experience.
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Cited by 1 Pith paper
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Progressive Binarization - Pauli Correlation Encoding: a Continuation Method for Constrained Optimization
An adaptive schedule that gradually increases the PCE binarization parameter solves budget-constrained MinCut up to 300 variables with 9-qubit circuits, reaching 88–100% constraint satisfaction.
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