Some graph optimization problems with weights satisfying linear constraints

Kameng Nip, Tianning Shi and Zhenbo Wang ()
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Kameng Nip: Xiamen University
Tianning Shi: Tsinghua University
Zhenbo Wang: Tsinghua University

Journal of Combinatorial Optimization, 2022, vol. 43, issue 1, No 10, 200-225

Abstract: Abstract In this paper, we study several graph optimization problems in which the weights of vertices or edges are variables determined by several linear constraints, including maximum matching problem under linear constraints (max-MLC), minimum perfect matching problem under linear constraints (min-PMLC), shortest path problem under linear constraints (SPLC) and vertex cover problem under linear constraints (VCLC). The objective of these problems is to decide the weights that are feasible to the linear constraints, and find the optimal solutions of corresponding graph optimization problems among all feasible choices of weights. We find that these problems are NP-hard and are hard to be approximated in general. These findings suggest us to explore various special cases of them. In particular, we show that when the number of constraints is a fixed constant, all these problems are polynomially solvable. Moreover, if the total number of distinct weights is a fixed constant, then max-MLC, min-PMLC and SPLC are polynomially solvable, and VCLC has a 2-approximation algorithm. In addition, we propose approximation algorithms for various cases of max-MLC.

Keywords: Graph optimization; Linear programming; Polynomial-time algorithm; Approximation algorithm; Computational complexity (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10878-021-00754-w

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