多获胜者确定的参数化复杂度：迈向固定参数可解性的更多探索
Parameterized Complexity of Multi-winner Determination: More Effort Towards Fixed-Parameter Tractability
AI总结：
针对三种主流k委员会选择规则的获胜者确定问题计算困难、现有参数化研究多为W[1]-难或W[2]-难的现状，研究通过引入更多自然单参数、组合参数及结构参数重新探究，旨在获取更多固定参数算法。
摘要：
我们研究了三种主流k委员会选择规则的获胜者确定问题的参数化复杂度，这三种规则分别是极小极大批准投票（minimax approval voting, MAV）、比例批准投票（proportional approval voting, PAV）以及Chamberlin-Courant批准投票（Chamberlin-Courant's approval voting, CCAV）。已知这些问题在计算上是困难的。尽管已有研究从参数化复杂度角度，针对若干自然参数对它们展开分析，但其中多数问题被证明是W[1]-难或W[2]-难的。为获取大量固定参数算法，我们通过考虑更多自然单参数、组合参数及结构参数，重新探究了这些问题。
英文摘要：
We study the parameterized complexity of winner determination problems for three prevalent $k$-committee selection rules, namely the minimax approval voting (MAV), the proportional approval voting (PAV), and the Chamberlin-Courant's approval voting (CCAV). It is known that these problems are computationally hard. Although they have been studied from the parameterized complexity point of view with respect to several natural parameters, many of them turned out to be W[1]-hard or W[2]-hard. Aiming at obtaining plentiful fixed-parameter algorithms, we revisit these problems by considering more natural single parameters, combined parameters, and structural parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
