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Journal articles on the topic 'Fair combinatorial optimization'

Author: Grafiati

Published: 7 September 2024

Last updated: 31 July 2025

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1

Bourdache, Nadjet, and Patrice Perny. "Active Preference Learning Based on Generalized Gini Functions: Application to the Multiagent Knapsack Problem." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7741–48. http://dx.doi.org/10.1609/aaai.v33i01.33017741.

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We consider the problem of actively eliciting preferences from a Decision Maker supervising a collective decision process in the context of fair multiagent combinatorial optimization. Individual preferences are supposed to be known and represented by linear utility functions defined on a combinatorial domain and the social utility is defined as a generalized Gini Social evaluation Function (GSF) for the sake of fairness. The GSF is a non-linear aggregation function parameterized by weighting coefficients which allow a fine control of the equity requirement in the aggregation of individual util

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Wang, Kai, Haoyu Liu, Zhipeng Hu, et al. "EnMatch: Matchmaking for Better Player Engagement via Neural Combinatorial Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9098–106. http://dx.doi.org/10.1609/aaai.v38i8.28760.

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Matchmaking is a core task in e-sports and online games, as it contributes to player engagement and further influences the game's lifecycle. Previous methods focus on creating fair games at all times. They divide players into different tiers based on skill levels and only select players from the same tier for each game. Though this strategy can ensure fair matchmaking, it is not always good for player engagement. In this paper, we propose a novel Engagement-oriented Matchmaking (EnMatch) framework to ensure fair games and simultaneously enhance player engagement. Two main issues need to be add

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MOULIN, HERVÉ. "COST SHARING IN NETWORKS: SOME OPEN QUESTIONS." International Game Theory Review 15, no. 02 (2013): 1340001. http://dx.doi.org/10.1142/s021919891340001x.

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The fertile application of cooperative game techniques to cost sharing problems on networks has so far concentrated on the Stand Alone core test of fairness and/or stability, and ignored many combinatorial optimization problems where this core can be empty. I submit there is much room for an axiomatic discussion of fair division in the latter problems, where Stand Alone objections are not implementable. But the computational complexity of optimal solutions is still a very severe obstacle to this approach.

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Adubi, Stephen A., Olufunke O. Oladipupo, and Oludayo O. Olugbara. "Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems." Algorithms 15, no. 11 (2022): 405. http://dx.doi.org/10.3390/a15110405.

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Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based i

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Sue, Jing Ren, Teong Chee Chuah, and Ying Loong Lee. "Binary Particle Swarm Optimization for Fair User Association in Network Slicing-Enabled Heterogeneous O-RANs." Journal of Engineering Technology and Applied Physics 6, no. 2 (2024): 16–24. http://dx.doi.org/10.33093/jetap.2024.6.2.3.

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The Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for a

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Maleš, Uroš, Dušan Ramljak, Tatjana Jakšić Krüger, Tatjana Davidović, Dragutin Ostojić, and Abhay Haridas. "Controlling the Difficulty of Combinatorial Optimization Problems for Fair Proof-of-Useful-Work-Based Blockchain Consensus Protocol." Symmetry 15, no. 1 (2023): 140. http://dx.doi.org/10.3390/sym15010140.

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The wide range of Blockchain (BC) applications and BC’s ubiquity come from the fact that BC, as a collection of records linked to each other, is strongly resistant to alteration, protected using cryptography, and maintained autonomously. All these benefits come with a cost, which in BC is expressed by a very high use of energy needed to execute consensus protocols. Traditionally, consensus protocols based on Proof-of-Work (PoW) ensure fairness, but are not very useful. The paradigm proposed in the recent literature, known as Proof-of-Useful-Work (PoUW), assumes the completion of additional use

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Wang, Zhenzhong, Qingyuan Zeng, Wanyu Lin, Min Jiang, and Kay Chen Tan. "Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21690–98. http://dx.doi.org/10.1609/aaai.v38i19.30168.

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A plethora of fair graph neural networks (GNNs) have been proposed to promote algorithmic fairness for high-stake real-life contexts. Meanwhile, explainability is generally proposed to help machine learning practitioners debug models by providing human-understandable explanations. However, seldom work on explainability is made to generate explanations for fairness diagnosis in GNNs. From the explainability perspective, this paper explores the problem of what subgraph patterns cause the biased behavior of GNNs, and what actions could practitioners take to rectify the bias? By answering the two

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Rokbani, Nizar, Pavel Kromer, Ikram Twir, and Adel M. Alimi. "A Hybrid Hierarchical Heuristic-ACO With Local Search Applied to Travelling Salesman Problem, AS-FA-Ls." International Journal of System Dynamics Applications 9, no. 3 (2020): 58–73. http://dx.doi.org/10.4018/ijsda.2020070104.

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The combinatorial optimization problem is attracting research because they have a wide variety of applications ranging from route planning and supply chain optimization to industrial scheduling and the IoT. Solving such problems using heuristics and bio-inspired techniques is an alternative to exact solutions offering acceptable solutions at fair computational costs. In this article, a new hierarchical hybrid method is proposed as a hybridization of Ant Colony Optimization (ACO), Firefly Algorithm (FA), and local search (AS-FA-Ls). The proposed methods are compared to similar techniques on the

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Kisel, Ivan, Robin Lakos та Gianna Zischka. "Deep-Learning-Based Optimization of the Signal/Background Ratio for Λ Particles in the CBM Experiment at FAIR". Algorithms 18, № 4 (2025): 229. https://doi.org/10.3390/a18040229.

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Machine learning algorithms have become essential tools in modern physics experiments, enabling the precise and efficient analysis of large-scale experimental data. The Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) demands innovative methods for processing the vast data volumes generated at high collision rates of up to 10 MHz. This study presents a deep-learning-based approach to enhance the signal/background (S/B) ratio for Λ particles within the Kalman Filter (KF) Particle Finder framework. Using the Artificial Neural Networks for First L

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Ostojić, Dragutin, Dušan Ramljak, Andrija Urošević, et al. "Systematic Literature Review of Optimization Algorithms for PCmax Problem||." Symmetry 17, no. 2 (2025): 178. https://doi.org/10.3390/sym17020178.

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In the era of open data and open science, it is important that, before announcing their new results, authors consider all previous studies and ensure that they have competitive material worth publishing. To save time, it is popular to replace the exhaustive search of online databases with the utilization of generative Artificial Intelligence (AI). However, especially for problems in niche domains, generative AI results may not be precise enough and sometimes can even be misleading. A typical example is P||Cmax, an important scheduling problem studied mainly in a wider context of parallel machi

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Lujak, Marin, Stefano Giordani, Andrea Omicini, and Sascha Ossowski. "Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation." Complexity 2020 (July 16, 2020): 1–21. http://dx.doi.org/10.1155/2020/1047369.

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One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature on scalable and dynamic task allocation focusing on deterministic and dynamic two-dimensional linear assignment problems. We focus on multiagent system representation of open vehicle fleets where dynamically appearing vehicles are represented by soft

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Li, Xuan Cindy, Yuelin Liu, Alejandro A. Schäffer, Stephen M. Mount, and S. Cenk Sahinalp. "Fair molecular feature selection unveils universally tumor lineage-informative methylation sites in colorectal cancer." Bioinformatics 41, Supplement_1 (2025): i150—i159. https://doi.org/10.1093/bioinformatics/btaf237.

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Abstract Motivation In the era of precision medicine, performing comparative analysis over diverse patient populations is a fundamental step toward tailoring healthcare interventions. However, the aspect of fairly selecting molecular features across multiple patients is often overlooked. Results To address this challenge, we introduce FALAFL (FAir muLti-sAmple Feature seLection), an algorithmic approach based on combinatorial optimization. FALAFL is designed to perform feature selection in sequencing data which ensures a balanced selection of features from all patient samples in a cohort. We h

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Li, Xia, and Buhong Wang. "Thinned Virtual Array for Cramer Rao Bound Optimization in MIMO Radar." International Journal of Antennas and Propagation 2021 (January 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/1408498.

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By transmitting multiple independent waveforms at the transmit side and processing echoes of spatial targets at the receive side, Multiple Input Multiple Output (MIMO) radar enjoys virtual array aperture expansion and more degree of freedom (DOF), both of which favors the application of direction finding or estimation of direction of arrival (DOA). The expanded virtual aperture provides higher angular resolution which also promotes the precision of DOA estimation, and the extra DOF brought by waveform diversity can be leveraged to focus energy in certain spatial region for better direction-fin

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Khaled, Smail, and Djebbar Bachir. "Electromagnetism-like mechanism algorithm for hybrid flow-shop scheduling problems." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 3 (2023): 1614. http://dx.doi.org/10.11591/ijeecs.v32.i3.pp1614-1620.

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<span>Given the interest and complexity, of the hybrid flow shops (HFS) problem in industry, he has been extensively considered, the HFS, is a complex combinatorial problem supported in many original world applications. We consider a hybrid flow shop FH4(P3, P2)||C<sub>max</sub> to applied in this paper. In this papers we attempt to optimize the makespan which refers to the last task completion time by an adequate meta-heuristic algorithm based on electromagnetism mechanism (EM). We also present analysis on the performance of the EM-algorithm adapted to HFS scheduling problem

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Alireza, Valipour, Yadollahi Mohammadreza, Rosli Mohamad Zin, Nordin Yahaya, and Norhazilan Md Noor. "An enhanced multi-objective optimization approach for risk allocation in public–private partnership projects: a case study of Malaysia." Canadian Journal of Civil Engineering 41, no. 2 (2014): 164–77. http://dx.doi.org/10.1139/cjce-2013-0342.

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The decision making for risk allocation problems in public–private partnership (PPP) projects is a vital process that directly affects the timeliness, cost, and quality of the project. Fair risk allocation is a vital factor to achieve success in the implementation of these projects. It is essential for private and public sectors to apply efficient risk allocation approaches to experience a more effective process of agreement arbitration and to reduce the appearance of dispute during the concession period. The aim of this study is to develop an optimization approach to enhance risk allocation p

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Eneko, Osaba, Yang Xin-She, Diaz Fernando, Lopez-Garcia Pedro, and Carballedo Roberto. "An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems." Engineering Applications of Artificial Intelligence 48 (November 25, 2015): 59–71. https://doi.org/10.1016/j.engappai.2015.10.006.

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Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric Traveling Salesman Problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obta

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Porter, Jason M., Marvin E. Larsen, J. Wesley Barnes, and John R. Howell. "Metaheuristic Optimization of a Discrete Array of Radiant Heaters." Journal of Heat Transfer 128, no. 10 (2006): 1031–40. http://dx.doi.org/10.1115/1.2345430.

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The design of radiant enclosures is an active area of research in radiation heat transfer. When design variables are discrete such as for radiant heater arrays with on-off control of individual heaters, current methods of design optimization fail. This paper reports the development of a metaheuristic thermal radiation optimization approach. Two metaheuristic optimization methods are explored: simulated annealing and tabu search. Both approaches are applied to a combinatorial radiant enclosure design problem. Configuration factors are used to develop a dynamic neighborhood for the tabu search a

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KUNER, PETER, and BIRGIT UEBERREITER. "PATTERN RECOGNITION BY GRAPH MATCHING—COMBINATORIAL VERSUS CONTINUOUS OPTIMIZATION." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 03 (1988): 527–42. http://dx.doi.org/10.1142/s0218001488000303.

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A generalization of subgraph isomorphism for the fault-tolerant interpretation of disturbed line images has been achieved. Object recognition is effected by optimal matching of a reference graph to the graph of a distorted image. This optimization is based on the solution of linear and quadratic assignment problems. The efficiency of the procedures developed for this objective has been proved in practical applications. NP-complete problems such as subgraph recognition need exhaustive computation if exact (branch-and-bound) algorithms are used. In contrast to this, heuristics are very fast and

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Schidler, André, and Stefan Szeider. "Extracting Problem Structure with LLMs for Optimized SAT Local Search." Proceedings of the International Symposium on Combinatorial Search 18 (July 19, 2025): 236–40. https://doi.org/10.1609/socs.v18i1.35999.

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Encoding combinatorial problems in terms of propositional satisfiability (SAT) enables utilization of highly efficient SAT solvers for combinatorial search. Local search preprocessing accelerates the SAT solver's search by providing high-quality starting points, a technique implemented in several modern SAT solvers. However, existing preprocessing methods employ generic strategies that fail to exploit the structural patterns inherent in problem encodings. This position paper proposes a novel paradigm wherein Large Language Models (LLMs) analyze problem encoding implementations to synthesize sp

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Singh, Vinay Kumar, and Vidushi Sharma. "Elitist Genetic Algorithm Based Energy Balanced Routing Strategy to Prolong Lifetime of Wireless Sensor Networks." Chinese Journal of Engineering 2014 (March 16, 2014): 1–6. http://dx.doi.org/10.1155/2014/437625.

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Wireless sensor networks have gained worldwide attention in recent years due to the advances made in wireless communication. Unequal energy dissipation causes the nodes to fail. The factors causing the unequal energy dissipation are, firstly, the distance between the nodes and base station and, secondly, the distance between the nodes themselves. Using traditional methods, it is difficult to obtain the high precision of solution as the problem is NP hard. The routing in wireless networks is a combinatorial optimization problem; hence, genetic algorithms can provide optimized solution to energy

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Rajeswari, Muniyan, Rajakumar Ramalingam, Shakila Basheer, Keerthi Samhitha Babu, Mamoon Rashid, and Ramar Saranya. "Multi-Objective ABC-NM Algorithm for Multi-Dimensional Combinatorial Optimization Problem." Axioms 12, no. 4 (2023): 395. http://dx.doi.org/10.3390/axioms12040395.

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This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail to satisfy constraints while achieving optimal performance. To address this issue, we propose a multi-objective artificial bee colony optimization algorithm with a classical multi-objective theme called fitness sharing. This approach helps the convergence of the Pareto solution set towards a single optimal solution that satisfies multiple objectiv

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Chen, Xiaosong, Hanqin Zhuang, Yang Liu, Huanle Xu, and Wing Cheong Lau. "Combinatorial Multi-Armed Bandits with Fairness Constraints: An Online Convex Optimization Perspective." Journal of Artificial Intelligence Research 82 (March 28, 2025): 1909–42. https://doi.org/10.1613/jair.1.16580.

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The problem of multi-armed bandit (MAB) with fairness constraints has emerged as an important research topic recently. For such problems, one common objective is to maximize the total rewards within a fixed number of pull rounds, while satisfying the fairness requirement of a minimum selection fraction for each individual arm in the long run. Previous works have made substantial advancements in designing various online selection solutions for MAB, however, when incorporating such fairness constraints, they fail to achieve a sublinear regret bound. In this paper, we study a combinatorial MAB pr

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Vallet, B., B. Soheilian, and M. Brédif. "Combinatorial clustering and Its Application to 3D Polygonal Traffic Sign Reconstruction From Multiple Images." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (August 7, 2014): 165–72. http://dx.doi.org/10.5194/isprsannals-ii-3-165-2014.

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The 3D reconstruction of similar 3D objects detected in 2D faces a major issue when it comes to grouping the 2D detections into clusters to be used to reconstruct the individual 3D objects. Simple clustering heuristics fail as soon as similar objects are close. This paper formulates a framework to use the geometric quality of the reconstruction as a hint to do a proper clustering. We present a methodology to solve the resulting combinatorial optimization problem with some simplifications and approximations in order to make it tractable. The proposed method is applied to the reconstruction of 3

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Ishiguro, Hisatoshi, and Kakuro Amasaka. "Proposal And Effectiveness Of A Highly Compelling Direct Mail Method - Establishment And Deployment Of PMOS-DM." International Journal of Management & Information Systems (IJMIS) 16, no. 1 (2011): 1. http://dx.doi.org/10.19030/ijmis.v16i1.6717.

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No clear processes are used at car dealers when deciding target customers for direct mail campaigns, and individual sales representatives rely on their experience when making such decisions. This means that dealer strategies lose their effectiveness and dealers fail to achieve the desired increase in customer visits. Thus, for this study, the author has established the Practical Method using Optimization and Statistics for Direct Mail (PMOS-DM) as a method of deciding the most suitable target customers for direct mail campaigns. Specifically, in order to both clarify the dealers target custome

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Kickmeier-Rust, Michael, and Andreas Holzinger. "Interactive Ant Colony Optimization to Support Adaptation in Serious Games." International Journal of Serious Games 6, no. 3 (2019): 37–50. http://dx.doi.org/10.17083/ijsg.v6i3.308.

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The success of serious games usually depends on their capabilities to engage learners and to provide them with personalized gaming and learning experiences. Therefore, it is important to equip a game, as an autonomous computer system, with a certain level of understanding about individual learning trajectories and gaming processes. AI and machine learning technologies increasingly enter the field; these technologies often fail, however, since serious games either pose highly complex problems (combining gaming and learning process) or do not provide the extensive data bases that would be requir

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Grinshpoun, T., A. Grubshtein, R. Zivan, A. Netzer, and A. Meisels. "Asymmetric Distributed Constraint Optimization Problems." Journal of Artificial Intelligence Research 47 (July 30, 2013): 613–47. http://dx.doi.org/10.1613/jair.3945.

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Distributed Constraint Optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. Many multi-agent problems include constraints that produce different gains (or costs) for the participating agents. Asymmetric gains of constrained agents cannot be naturally represented by the standard DCOP model. The present paper proposes a general framework for Asymmetric DCOPs (ADCOPs). In ADCOPs different agents may have different valuations for constraints that they are involved in. The new f

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Ranjan, Rajesh, and Jitender Kumar Chhabra. "A Modified Binary Arithmetic Optimization Algorithm for Feature Selection." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 11 (July 25, 2023): 199–205. http://dx.doi.org/10.37394/232018.2023.11.18.

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Feature selection chooses the optimal subset from the feature set without scarifying the information carried by the dataset. It is considered a complex combinatorial problem, so classical optimization techniques fail to solve it when the feature set becomes larger. Meta-heuristic approaches are well known to solve complex optimization problems; hence these algorithms have been successfully applied to extract optimal feature subsets. The arithmetic Optimization Algorithm is a newly proposed mathematics-based meta-heuristic search algorithm successfully applied to solve optimization problems. Ho

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Botea, Adi, and Vadim Bulitko. "Scaling Up Search with Partial Initial States in Optimization Crosswords." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (2021): 20–27. http://dx.doi.org/10.1609/socs.v12i1.18547.

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Heuristic search remains a leading approach to difficult combinatorial optimization problems. Search algorithms can utilize pruning based on comparing a target score with an admissible (optimistic) estimate of the best score that can be achieved from a given state. If the former is larger they prune the state. However, when the target score is too high the search can fail by exhausting the space without finding a solution. In this paper we show that such failed searches can still be valuable. Specifically, best partial solutions encountered in such failed searches can often bear a high similar

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Crawford, Broderick, Ricardo Soto, Eric Monfroy, Carlos Castro, Wenceslao Palma, and Fernando Paredes. "A Hybrid Soft Computing Approach for Subset Problems." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/716069.

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Subset problems (set partitioning, packing, and covering) are formal models for many practical optimization problems. A set partitioning problem determines how the items in one set (S) can be partitioned into smaller subsets. All items inSmust be contained in one and only one partition. Related problems are set packing (all items must be contained in zero or one partitions) and set covering (all items must be contained in at least one partition). Here, we present a hybrid solver based on ant colony optimization (ACO) combined with arc consistency for solving this kind of problems. ACO is a swa

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Hevapathige, Asela, Qing Wang, and Ahad N. Zehmakan. "DeepSN: A Sheaf Neural Framework for Influence Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17177–85. https://doi.org/10.1609/aaai.v39i16.33888.

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Influence maximization is a key topic in data mining, with broad applications in social network analysis and viral marketing. In recent years, researchers have increasingly turned to machine learning techniques to address this problem. By learning the underlying diffusion processes from data, these methods improve the generalizability of solutions while optimizing objectives to identify the optimal seed set for maximizing influence. Nonetheless, two fundamental challenges remain unresolved: (1) While Graph Neural Networks (GNNs) are increasingly employed to learn diffusion models, their tradit

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Akagi, Yasunori, Takuya Nishimura, Yusuke Tanaka, Takeshi Kurashima, and Hiroyuki Toda. "Exact and Efficient Inference for Collective Flow Diffusion Model via Minimum Convex Cost Flow Algorithm." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3163–70. http://dx.doi.org/10.1609/aaai.v34i04.5713.

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Collective Flow Diffusion Model (CFDM) is a general framework to find the hidden movements underlying aggregated population data. The key procedure in CFDM analysis is MAP inference of hidden variables. Unfortunately, existing approaches fail to offer exact MAP inferences, only approximate versions, and take a lot of computation time when applied to large scale problems. In this paper, we propose an exact and efficient method for MAP inference in CFDM. Our key idea is formulating the MAP inference problem as a combinatorial optimization problem called Minimum Convex Cost Flow Problem (C-MCFP)

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Glynn, Peter W., Andrey Dolgin, Reuven Y. Rubinstein, and Radislav Vaisman. "HOW TO GENERATE UNIFORM SAMPLES ON DISCRETE SETS USING THE SPLITTING METHOD." Probability in the Engineering and Informational Sciences 24, no. 3 (2010): 405–22. http://dx.doi.org/10.1017/s0269964810000057.

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The goal of this work is twofold. We show the following:1.In spite of the common consensus on the classic Markov chain Monte Carlo (MCMC) as a universal tool for generating samples on complex sets, it fails to generate points uniformly distributed on discrete ones, such as that defined by the constraints of integer programming. In fact, we will demonstrate empirically that not only does it fail to generate uniform points on the desired set, but typically it misses some of the points of the set.2.Thesplitting, also called thecloningmethod – originally designed for combinatorial optimization and

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Berman, Kenneth A., and Chad Yoshikawa. "Why locally-fair maximal flows in client-server networks perform well." Journal of Combinatorial Optimization 22, no. 3 (2010): 426–37. http://dx.doi.org/10.1007/s10878-010-9321-y.

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Farhi, Edward, Jeffrey Goldstone, Sam Gutmann, and Leo Zhou. "The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size." Quantum 6 (July 7, 2022): 759. http://dx.doi.org/10.22331/q-2022-07-07-759.

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The Quantum Approximate Optimization Algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization problems whose performance can only improve with the number of layers p. While QAOA holds promise as an algorithm that can be run on near-term quantum computers, its computational power has not been fully explored. In this work, we study the QAOA applied to the Sherrington-Kirkpatrick (SK) model, which can be understood as energy minimization of n spins with all-to-all random signed couplings. There is a recent classical algorithm by Montanari that, assuming a widely believed con

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Ghosh, Bishwamittra, Dmitry Malioutov, and Kuldeep S. Meel. "Efficient Learning of Interpretable Classification Rules." Journal of Artificial Intelligence Research 74 (August 30, 2022): 1823–63. http://dx.doi.org/10.1613/jair.1.13482.

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Machine learning has become omnipresent with applications in various safety-critical domains such as medical, law, and transportation. In these domains, high-stake decisions provided by machine learning necessitate researchers to design interpretable models, where the prediction is understandable to a human. In interpretable machine learning, rule-based classifiers are particularly effective in representing the decision boundary through a set of rules comprising input features. Examples of such classifiers include decision trees, decision lists, and decision sets. The interpretability of rule-

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Yang, Lei, Xi Yu, Jiannong Cao, Xuxun Liu, and Pan Zhou. "Exploring Deep Reinforcement Learning for Task Dispatching in Autonomous On-Demand Services." ACM Transactions on Knowledge Discovery from Data 15, no. 3 (2021): 1–23. http://dx.doi.org/10.1145/3442343.

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Autonomous on-demand services, such as GOGOX (formerly GoGoVan) in Hong Kong, provide a platform for users to request services and for suppliers to meet such demands. In such a platform, the suppliers have autonomy to accept or reject the demands to be dispatched to him/her, so it is challenging to make an online matching between demands and suppliers. Existing methods use round-based approaches to dispatch demands. In these works, the dispatching decision is based on the predicted response patterns of suppliers to demands in the current round, but they all fail to consider the impact of futur

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Wang, Wei, and Tongbin Li. "Optimization of Nursing Care Resource Allocation in an Aging Population Based on Combinatorial Optimization Algorithm." Applied Mathematics and Nonlinear Sciences, October 28, 2023. http://dx.doi.org/10.2478/amns.2023.2.00823.

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Abstract This paper first considers population aging as a breakthrough, analyzes the demand for long-term care due to population aging, and classifies the models and characteristics of long-term care. Secondly, the combinatorial optimization algorithm and its classical cases are analyzed, and a care resource allocation system is built based on the solution set performance metrics and multi-objective combinatorial optimization algorithm for an aging population society. In city A, a research experiment examines the elderly population and the allocation of nursing care resources. The results show

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Kawase, Yasushi, and Hanna Sumita. "Randomized Strategies for Robust Combinatorial Optimization with Approximate Separation." Algorithmica, October 10, 2023. http://dx.doi.org/10.1007/s00453-023-01175-3.

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AbstractIn this paper, we study the following robust optimization problem. Given a set family representing feasibility and candidate objective functions, we choose a feasible set, and then an adversary chooses one objective function, knowing our choice. The goal is to find a randomized strategy (i.e., a probability distribution over the feasible sets) that maximizes the expected objective value in the worst case. This problem is fundamental in wide areas such as artificial intelligence, machine learning, game theory, and optimization. To solve the problem, we provide a general framework based

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Pelofske, Elijah. "Biased degenerate ground-state sampling of small Ising models with converged quantum approximate optimization algorithm." Physical Review E 111, no. 5 (2025). https://doi.org/10.1103/physreve.111.054103.

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The quantum alternating operator ansatz, a generalization of the quantum approximate optimization algorithm (QAOA), is a quantum algorithm used for approximately solving combinatorial optimization problems. QAOA typically uses the transverse field mixer as the driving Hamiltonian. One of the interesting properties of the transverse field driving Hamiltonian is that it results in nonuniform sampling of degenerate ground states of optimization problems. In this study, we numerically examine the fair sampling properties of the transverse field mixer QAOA, and Grover mixer QAOA (GM-QAOA), which pr

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Li, Su, Hrayer Aprahamian, Maher Nouiehed, and Hadi El-Amine. "An Optimization-Based Order-and-Cut Approach for Fair Clustering of Data Sets." INFORMS Journal on Data Science, August 16, 2023. http://dx.doi.org/10.1287/ijds.2022.0005.

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Machine learning algorithms have been increasingly integrated into applications that significantly affect human lives. This surged an interest in designing algorithms that train machine learning models to minimize training error and imposing a certain level of fairness. In this paper, we consider the problem of fair clustering of data sets. In particular, given a set of items each associated with a vector of nonsensitive attribute values and a categorical sensitive attribute (e.g., gender, race, etc.), our goal is to find a clustering of the items that minimizes the loss (i.e., clustering obje

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Ali, Sura Mazin, Noor Thamer Mahmood, and Samer Amil Yousif. "Meerkat Clan Algorithm for Solving N-Queen Problems." Iraqi Journal of Science, July 1, 2021, 2082–89. http://dx.doi.org/10.24996/ijs.2021.62.6.33.

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The swarm intelligence and evolutionary methods are commonly utilized by researchers in solving the difficult combinatorial and Non-Deterministic Polynomial (NP) problems. The N-Queen problem can be defined as a combinatorial problem that became intractable for the large ‘n’ values and, thereby, it is placed in the NP class of problems. In the present study, a solution is suggested for the N-Queen problem, on the basis of the Meerkat Clan Algorithm (MCA). The problem of n-Queen can be mainly defined as one of the generalized 8-Queen problem forms, for which the aim is placing 8 queens in a way

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Bassett, Kimberly L., Tylan Watkins, Jonathan Coleman, et al. "A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films." Integrating Materials and Manufacturing Innovation, October 26, 2023. http://dx.doi.org/10.1007/s40192-023-00315-5.

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AbstractFuture machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategi

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Neria, Gal, and Michal Tzur. "The Dynamic Pickup and Allocation with Fairness Problem." Transportation Science, May 30, 2024. http://dx.doi.org/10.1287/trsc.2023.0228.

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Urban logistic applications that involve pickup and distribution of goods require making routing and allocation decisions with respect to a set of sites. In cases where the supply quantities and the time in which they become available are unknown in advance, these decisions must be determined in real time based on information that arrives gradually. Furthermore, in many applications that satisfy the described setting, fair allocation is desired in addition to system effectiveness. In this paper, we consider the problem of determining a vehicle route that visits two types of sites in any order:

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Patel, Yash J., Sofiene Jerbi, Thomas Bäck, and Vedran Dunjko. "Reinforcement learning assisted recursive QAOA." EPJ Quantum Technology 11, no. 1 (2024). http://dx.doi.org/10.1140/epjqt/s40507-023-00214-w.

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AbstractIn recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for w

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Patel, Yash J., Sofiene Jerbi, Thomas Bäck, and Vedran Dunjko. "Reinforcement Learning Assisted Recursive QAOA." EPJ Quantum Technology 11, no. 6 (2024). https://doi.org/10.1140/epjqt/s40507-023-00214-w.

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In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope ofusing NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance.To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOAhas been studied comparatively less than QAOA, and it is less understood, for instance, for what family

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Okamoto, Yasuharu. "Maximizing gerrymandering through ising model optimization." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-03050-z.

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AbstractBy using the Ising model formulation for combinatorial optimization with 0–1 binary variables, we investigated the extent to which partisan gerrymandering is possible from a random but even distribution of supporters. Assuming that an electoral district consists of square subareas and that each subarea shares at least one edge with other subareas in the district, it was possible to find the most tilted assignment of seats in most cases. However, in cases where supporters' distribution included many enclaves, the maximum tilted assignment was usually found to fail. We also discussed the

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Coudert, David, Stéphane Pérennes, Hervé Rivano, and Marie-Emilie Voge. "Combinatorial optimization in networks with Shared Risk Link Groups." Discrete Mathematics & Theoretical Computer Science Vol. 18 no. 3, Distributed Computing and... (2016). http://dx.doi.org/10.46298/dmtcs.1297.

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The notion of Shared Risk Link Groups (SRLG) captures survivability issues when a set of links of a network may fail simultaneously. The theory of survivable network design relies on basic combinatorial objects that are rather easy to compute in the classical graph models: shortest paths, minimum cuts, or pairs of disjoint paths. In the SRLG context, the optimization criterion for these objects is no longer the number of edges they use, but the number of SRLGs involved. Unfortunately, computing these combinatorial objects is NP-hard and hard to approximate with this objective in general. Never

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Gebler, Colin, Jochen Rethmann, and Peer Ueberholz. "QUBO Models for the FIFO Stack-Up Problem and Experimental Evaluation on a Quantum Annealer." SN Computer Science 5, no. 7 (2024). http://dx.doi.org/10.1007/s42979-024-03082-y.

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AbstractQuantum annealing has been applied to combinatorial optimization problems in recent years. In this paper we study the possibility to use quantum annealing for solving the combinatorial FIFO Stack-Up problem, where bins have to be stacked-up from a conveyor belt onto pallets. The problem is NP-hard and can be solved using linear programming approaches. We developed two QUBO (quadratic unconstrained binary optimization) objective functions based on a bin stack-up solution and a pallet stack-up solution for this problem suitable for a quantum annealer. The number of variables was minimize

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Oki, Taihei. "Improved structural methods for nonlinear differential-algebraic equations via combinatorial relaxation." IMA Journal of Numerical Analysis, December 22, 2021. http://dx.doi.org/10.1093/imanum/drab094.

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Abstract Differential-algebraic equations (DAEs) are widely used for modelling dynamical systems. In the numerical analysis of DAEs, consistent initialization and index reduction are important preprocessing steps prior to numerical integration. Existing DAE solvers commonly adopt structural preprocessing methods based on combinatorial optimization. Unfortunately, structural methods fail if the DAE has a singular system Jacobian matrix. For such DAEs, methods have been proposed to modify them to other DAEs to which structural methods are applicable, based on the combinatorial relaxation techniq

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Bürgisser, Peter, and Christian Ikenmeyer. "A max-flow algorithm for positivity of Littlewood-Richardson coefficients." Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings vol. AK,..., Proceedings (2009). http://dx.doi.org/10.46298/dmtcs.2749.

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International audience Littlewood-Richardson coefficients are the multiplicities in the tensor product decomposition of two irreducible representations of the general linear group $\mathrm{GL}(n,\mathbb{C})$. They have a wide variety of interpretations in combinatorics, representation theory and geometry. Mulmuley and Sohoni pointed out that it is possible to decide the positivity of Littlewood-Richardson coefficients in polynomial time. This follows by combining the saturation property of Littlewood-Richardson coefficients (shown by Knutson and Tao 1999) with the well-known fact that linear o

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