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Showing papers in "Journal of Combinatorial Optimization in 2023"



Journal ArticleDOI
TL;DR: In this article , a non-monotone submodular maximization problem subject to novel group fairness constraints is studied, where the goal is to select a set of items that maximizes the non-modular function while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker.
Abstract: Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large dataset. However, data items might have sensitive attributes such as race or gender, in this setting, it is important to design fairness-aware algorithms to mitigate potential algorithmic bias that may cause over- or under- representation of particular groups. Motivated by that, we propose and study the classic non-monotone submodular maximization problem subject to novel group fairness constraints. Our goal is to select a set of items that maximizes a non-monotone submodular function, while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker. We develop the first constant-factor approximation algorithms for this problem. We also extend the basic model to incorporate an additional global size constraint on the total number of selected items.

2 citations




Journal ArticleDOI
TL;DR: In this paper , the optimal dynamic rationing policy must be of transformational threshold type, and sufficient conditions under each of which the policy is of threshold type (i.e., critical rationing level).
Abstract: In this paper, we study a stock-rationing queue with two demand classes by means of the sensitivity-based optimization, and develop a complete algebraic solution to the optimal dynamic rationing policy. We show that the optimal dynamic rationing policy must be of transformational threshold type. Based on this finding, we can refine three sufficient conditions under each of which the optimal dynamic rationing policy is of threshold type (i.e., critical rationing level). To do this, we use the performance difference equation to characterize the monotonicity and optimality of the long-run average profit of this system, and thus establish some new structural properties of the optimal dynamic rationing policy by observing any given reference policy. Finally, we use numerical experiments to demonstrate our theoretical results of the optimal dynamic rationing policy. We believe that the methodology and results developed in this paper can shed light on the study of stock-rationing queue and open a series of potentially promising research.

2 citations



Journal ArticleDOI
TL;DR: In this paper , the authors introduce a unified framework for randomized subset selection that incorporates group fairness constraints, where a global utility function and a set of group utility functions for each group are considered.
Abstract: Machine learning algorithms play an important role in a variety of important decision-making processes, including targeted advertisement displays, home loan approvals, and criminal behavior predictions. Given the far-reaching impact of these algorithms, it is crucial that they operate fairly, free from bias or prejudice towards certain groups in the population. Ensuring impartiality in these algorithms is essential for promoting equality and avoiding discrimination. To this end we introduce a unified framework for randomized subset selection that incorporates group fairness constraints. Our problem involves a global utility function and a set of group utility functions for each group, here a group refers to a group of individuals (e.g., people) sharing the same attributes (e.g., gender). Our aim is to generate a distribution across feasible subsets, specifying the selection probability of each feasible set, to maximize the global utility function while meeting a predetermined quota for each group utility function in expectation. Note that there may not necessarily be any direct connections between the global utility function and each group utility function. We demonstrate that this framework unifies and generalizes many significant applications in machine learning and operations research. Our algorithmic results either improves the best known result or provide the first approximation algorithms for new applications.

1 citations
























Journal ArticleDOI
Huazhong Lü1
TL;DR: The anti-Kekul\'{e} number is defined as the smallest number of edges whose deletion results in a subgraph without perfect matchings such that each component has at least $s+1$ vertices as mentioned in this paper .
Abstract: The anti-Kekul\'{e} number of a connected graph $G$ is the smallest number of edges whose deletion results in a connected subgraph having no Kekul\'{e} structures (perfect matchings). As a common generalization of (conditional) matching preclusion number and anti-Kekul\'{e} number of a graph $G$, we introduce $s$-restricted matching preclusion number of $G$ as the smallest number of edges whose deletion results in a subgraph without perfect matchings such that each component has at least $s+1$ vertices. In this paper, we first show that conditional matching preclusion problem and anti-Kekul\'{e} problem are NP-complete, respectively, then generalize this result to $s$-restricted matching preclusion problem. Moreover, we give some sufficient conditions to compute $s$-restricted matching preclusion numbers of regular graphs. As applications, $s$-restricted matching preclusion numbers of complete graphs, hypercubes and hyper Petersen networks are determined.