关于枚举权重不精确的非支配矩阵

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tom Davot , Tuan-Anh Vu , Sébastien Destercke , David Savourey
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引用次数: 0

摘要

稳健组合优化的许多研究都考虑了区间值成本或约束条件。虽然这些研究大多侧重于按照最小值等稳健标准找到唯一的解决方案,但也有少数研究考虑了可能最优解决方案集的特征问题。本文就属于这一研究领域,并考虑了在区间值成本条件下精确枚举可能最优矩阵集合的问题。我们特别指出,这个集合中的每个解都可以通过多项式过程获得,并提供了一种实现枚举的高效算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the enumeration of non-dominated matroids with imprecise weights

Many works within robust combinatorial optimisation consider interval-valued costs or constraints. While most of these works focus on finding a unique solution following a robust criteria such as minimax, a few consider the problem of characterising a set of possibly optimal solutions. This paper is situated within this line of work, and considers the problem of exactly enumerating the set of possibly optimal matroids under interval-valued costs. We show in particular that each solution in this set can be obtained through a polynomial procedure, and provide an efficient algorithm to achieve the enumeration.

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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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