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Journal articles on the topic 'Computational-hard real-life problem'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Iantovics, Laszlo Barna, László Kovács, and Corina Rotar. "MeasApplInt - a novel intelligence metric for choosing the computing systems able to solve real-life problems with a high intelligence." Applied Intelligence 49 (April 1, 2019): 3491–511. https://doi.org/10.1007/s10489-019-01440-5.

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Intelligent agent-based systems are applied for many real-life difficult problem-solving tasks in domains like transport and healthcare. In the case of many classes of real-life difficult problems, it is important to make an efficient selection of the computing systems that are able to solve the problems very intelligently. The selection of the appropriate computing systems should be based on an intelligence metric that is able to measure the systems intelligence for real-life problem solving. In this paper, we propose a novel universal metric called <em>MeasApplInt</em> able to meas

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Konstantakopoulos, Grigorios D., Sotiris P. Gayialis, Evripidis P. Kechagias, Georgios A. Papadopoulos, and Ilias P. Tatsiopoulos. "A Multiobjective Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Time Windows." Algorithms 13, no. 10 (2020): 243. http://dx.doi.org/10.3390/a13100243.

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The Vehicle Routing Problem with Time Windows (VRPTW) is an NP-Hard optimization problem which has been intensively studied by researchers due to its applications in real-life cases in the distribution and logistics sector. In this problem, customers define a time slot, within which they must be served by vehicles of a standard capacity. The aim is to define cost-effective routes, minimizing both the number of vehicles and the total traveled distance. When we seek to minimize both attributes at the same time, the problem is considered as multiobjective. Although numerous exact, heuristic and m

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Joshi, Rajendra Prasad. "Analysis of Metaheuristic Solutions to the Response Time Variability Problem." Api Journal of Science 1 (December 31, 2024): 81–83. https://doi.org/10.3126/ajs.v1i1.75493.

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The problem of variation in the response time is known as response time variability problem (RTVP). It is combinatorial NP-hard problem which has a broad range of real-life applications. The RTVP arises whenever events, jobs, clients or products need to be sequenced so as to minimize the variability of the time they wait for their next turn in obtaining the resources they need to advance. In RTVP the concern is to find out near optimal sequence of jobs with objective of minimizing the response time variability. The metaheuristic approaches to solve the RTVP are: Multi-start (MS), Greedy Random

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Hidri, Lotfi, and Ahmed M. Elsherbeeny. "Optimal Solution to the Two-Stage Hybrid Flow Shop Scheduling Problem with Removal and Transportation Times." Symmetry 14, no. 7 (2022): 1424. http://dx.doi.org/10.3390/sym14071424.

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The two-stage hybrid flow shop scheduling problem with removal and transportation times is addressed in this paper. The maximum completion time is the objective function to be minimized. This scheduling problem is modeling real-life situations encountered in manufacturing and industrial areas. On the other hand, the studied problem is a challenging one from a theoretical point of view since it is NP-Hard in a strong sense. In addition, the problem is symmetric in the following sense. Scheduling from the second stage to the first provides the same optimal solution as the studied problem. This p

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Zhang, Wenze, and Chenyang Xu. "A comparative study between SA and GA in solving MTSP." Theoretical and Natural Science 18, no. 1 (2023): 61–70. http://dx.doi.org/10.54254/2753-8818/18/20230321.

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The multiple traveling salesmen problems (MTSP) is a combinatorial optimization and np-hard problem. In practice, the computational resource required to solve such problems is usually prohibitive, and, in most cases, using heuristic algorithms is the only practical option. This paper implements genetic algorithms (GA) and simulated annealing (SA) to solve the MTSP and does an experimental study based on a benchmark from the TSPLIB instance to compare the performance of two algorithms in reality. The results show that GA can achieve an acceptable solution in a shorter time for any of the MTSP c

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Kwan, Raymond S. K., Ann S. K. Kwan, and Anthony Wren. "Evolutionary Driver Scheduling with Relief Chains." Evolutionary Computation 9, no. 4 (2001): 445–60. http://dx.doi.org/10.1162/10636560152642869.

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Public transport driver scheduling problems are well known to be NP-hard. Although some mathematically based methods are being used in the transport industry, there is room for improvement. A hybrid approach incorporating a genetic algorithm (GA) is presented. The role of the GA is to derive a small selection of good shifts to seed a greedy schedule construction heuristic. A group of shifts called a relief chain is identi-fied and recorded. The relief chain is then inherited by the offspring and used by the GA for schedule construction. The new approach has been tested using real-life data set

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Yaşar, Abdurrahman, Muhammed Fati̇h Balin, Xiaojing An, Kaan Sancak, and Ümit V. Çatalyürek. "On Symmetric Rectilinear Partitioning." ACM Journal of Experimental Algorithmics 27 (December 31, 2022): 1–26. http://dx.doi.org/10.1145/3523750.

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Even distribution of irregular workload to processing units is crucial for efficient parallelization in many applications. In this work, we are concerned with a spatial partitioning called rectilinear partitioning (also known as generalized block distribution). More specifically, we address the problem of symmetric rectilinear partitioning of two dimensional domains, and utilize sparse matrices to model them. By symmetric, we mean both dimensions (i.e., the rows and columns of the matrix) are identically partitioned yielding a tiling where the diagonal tiles (blocks) will be squares. We first

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Yang, Yajun, Zhongfei Li, Xin Wang, and Qinghua Hu. "Finding the Shortest Path with Vertex Constraint over Large Graphs." Complexity 2019 (February 19, 2019): 1–13. http://dx.doi.org/10.1155/2019/8728245.

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Graph is an important complex network model to describe the relationship among various entities in real applications, including knowledge graph, social network, and traffic network. Shortest path query is an important problem over graphs and has been well studied. This paper studies a special case of the shortest path problem to find the shortest path passing through a set of vertices specified by user, which is NP-hard. Most existing methods calculate all permutations for given vertices and then find the shortest one from these permutations. However, the computational cost is extremely expens

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Peng, Liwen, and Yongguo Liu. "Feature Selection and Overlapping Clustering-Based Multilabel Classification Model." Mathematical Problems in Engineering 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/2814897.

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Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning. Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework. In this paper, we explore the clustering-based MLC problem. Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy. In this study

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10

Withers, P. J., and T. M. Holden. "Diagnosing Engineering Problems with Neutrons." MRS Bulletin 24, no. 12 (1999): 17–23. http://dx.doi.org/10.1557/s0883769400053677.

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In the past, many unexpected failures of components were due to poor quality control or a failure to calculate—or to miscalculate—the stresses or fatigue stresses a component would experience in service. Today, improved manufacturing, fracture mechanics, and computational finite element methods combine to provide a solid framework for reducing safety factors, enabling leaner design. In this context, residual stress—that is, stress that equilibrates within the structure and is always present at some level due to manufacturing—presents a real problem. It is difficult to predict and as hard to me

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Xu, Dong, and Nazrul I. Shaikh. "A Heuristic Approach for Ranking Items Based on Inputs from Multiple Experts." International Journal of Information Systems and Social Change 9, no. 3 (2018): 1–22. http://dx.doi.org/10.4018/ijissc.2018070101.

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This article describes how rank aggregation focuses on synthesizing a single ranked list based on rankings supplied by multiple judges. Such aggregations are widely applied in the areas of information retrieval, web search, and data mining. The problem of rank aggregation has been shown to be NP-hard and this article presents a heuristic approach to create an aggregated ranking score for all items on the lists. The proposed heuristic is scalable and performs. A computational study, as well as a real-life study involving the ranking of 147 engineering colleges in the US is presented to elucidat

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Park, Dongjoo, Laurence R. Rilett, and Changho Choi. "A class of multicriteria shortest path problems for real-time in-vehicle routing." Canadian Journal of Civil Engineering 34, no. 9 (2007): 1096–109. http://dx.doi.org/10.1139/l07-013.

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In-route guidance systems fastest path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this paper is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e., Pareto optimal) paths for real-time in-vehicle routing. The basic notion of the proposed approach is that (i) enumerating all nondominated paths is computationally too expensive, (ii) obtaining a stable mathematical representation of the

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Boixel, Arthur, and Ronald De Haan. "On the Complexity of Finding Justifications for Collective Decisions." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 5194–201. http://dx.doi.org/10.1609/aaai.v35i6.16656.

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In a collective decision-making process, having the possibility to provide non-expert agents with a justification for why a target outcome is a good compromise given their individual preferences, is an appealing idea. Such questions have recently been addressed in the computational social choice community at large---whether it was to explain the outcomes of a specific rule in voting theory or to seek transparency and accountability in multi-criteria decision making. Ultimately, the development of real-life applications based on these notions depends on their practical feasibility and on the sc

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Gaar, Elisabeth, Melanie Siebenhofer, and Angelika Wiegele. "An SDP-based approach for computing the stability number of a graph." Mathematical Methods of Operations Research 95, no. 1 (2022): 141–61. http://dx.doi.org/10.1007/s00186-022-00773-1.

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AbstractFinding the stability number of a graph, i.e., the maximum number of vertices of which no two are adjacent, is a well known NP-hard combinatorial optimization problem. Since this problem has several applications in real life, there is need to find efficient algorithms to solve this problem. Recently, Gaar and Rendl enhanced semidefinite programming approaches to tighten the upper bound given by the Lovász theta function. This is done by carefully selecting some so-called exact subgraph constraints (ESC) and adding them to the semidefinite program of computing the Lovász theta function.

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15

Eberbach, Eugene. "$-Calculus of Bounded Rational Agents: Flexible Optimization as Search under Bounded Resources in Interactive Systems." Fundamenta Informaticae 68, no. 1-2 (2005): 47–102. https://doi.org/10.3233/fun-2005-681-203.

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This paper presents a novel model for resource bounded computation based on process algebras. Such model is called the $-calculus (cost calculus). Resource bounded computation attempts to find the best answer possible given operational constraints. The $-calculus provides a uniform representation for optimization in the presence of limited resources. It uses cost-optimization to find the best quality solutions while using a minimal amount of resources. A unique aspect of the approach is to propose a resource bounded process algebra as a generic problem solving paradigm targeting interactive AI

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Abdelmaguid , Tamer F. "Bi-Objective, Dynamic, Multiprocessor Open-Shop Scheduling: A Hybrid Scatter Search–Tabu Search Approach." Algorithms 17, no. 8 (2024): 371. http://dx.doi.org/10.3390/a17080371.

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This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan) and the minimization of the mean weighted flow time. Both are particularly important for improving machines’ utilization and customer satisfaction level in maintenance and healthcare diagnostic systems, in which the studied Bi-DMOSP is mostly encountered. Since the studied problem is NP-hard for both objectives, fast algorithms are needed to fulfi

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László, Barna Iantovics. "Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring." Mathematics 9, no. 6 (2022): 681. https://doi.org/10.3390/math9060681.

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Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of

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Iantovics, László Barna, Roumen Kountchev, and Gloria Cerasela Crișan. "ExtrIntDetect—A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence." Symmetry 11, no. 9 (2019): 1123. https://doi.org/10.3390/sym11091123.

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In this research, we define a specific type of performance of the intelligent agent-based systems (IABSs) in terms of a difficult problem-solving intelligence measure. Many studies present the successful application of intelligent cooperative multiagent systems (ICMASs) for efficient, flexible and robust solving of difficult real-life problems. Based on a comprehensive study of the scientific literature, we conclude that there is no unanimous view in the scientific literature on machine intelligence, or on what an intelligence metric must measure. Metrics presented in the scientific literature

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Bhaskaran, S., Raja Marappan, and B. Santhi. "Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets." Mathematics 8, no. 7 (2020): 1106. http://dx.doi.org/10.3390/math8071106.

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Nowadays, because of the tremendous amount of information that humans and machines produce every day, it has become increasingly hard to choose the more relevant content across a broad range of choices. This research focuses on the design of two different intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders. In the first method, the modified cluster based intelligent collaborative filtering is applied with the sequential clustering that operates on the values of dataset,

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Cicerone, Serafino, and Gabriele Di Stefano. "Special Issue on “Graph Algorithms and Applications”." Algorithms 14, no. 5 (2021): 150. http://dx.doi.org/10.3390/a14050150.

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The mixture of data in real life exhibits structure or connection property in nature. Typical data include biological data, communication network data, image data, etc. Graphs provide a natural way to represent and analyze these types of data and their relationships. For instance, more recently, graphs have found new applications in solving problems for emerging research fields such as social network analysis, design of robust computer network topologies, frequency allocation in wireless networks, and bioinformatics. Unfortunately, the related algorithms usually suffer from high computational

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21

Sbai, Ines, and Saoussen Krichen. "A Novel Adaptive Genetic Algorithm for Dynamic Vehicle Routing Problem With Backhaul and Two-Dimensional Loading Constraints." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–34. http://dx.doi.org/10.4018/ijamc.2022010103.

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In this paper, we consider an extension of the Dynamic Vehicle Routing Problem with Backhauls integrated with two-dimensional loading problem called DVRPB with 2D loading constraints (2L-DVRPB). In the VRPB, a vehicle can deliver (Linehaul) then collect goods from customers (backhaul) and bring back to the depot. Once customer demand is formed by a set of two-dimensional items the problem will be treat as a 2L-VRPB. The 2L-VRPB has been studied on the static case. However, in most real-life application, new customer requests can be happen over time of backhaul and thus perturb the optimal rout

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Rico, Noelia, Camino R. Vela, Raúl Pérez-Fernández, and Irene Díaz. "Reducing the Computational Time for the Kemeny Method by Exploiting Condorcet Properties." Mathematics 9, no. 12 (2021): 1380. http://dx.doi.org/10.3390/math9121380.

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Preference aggregation and in particular ranking aggregation are mainly studied by the field of social choice theory but extensively applied in a variety of contexts. Among the most prominent methods for ranking aggregation, the Kemeny method has been proved to be the only one that satisfies some desirable properties such as neutrality, consistency and the Condorcet condition at the same time. Unfortunately, the problem of finding a Kemeny ranking is NP-hard, which prevents practitioners from using it in real-life problems. The state of the art of exact algorithms for the computation of the Ke

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Cheikh, Salmi, and Jessie J. Walker. "Solving Task Scheduling Problem in the Cloud Using a Hybrid Particle Swarm Optimization Approach." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–25. http://dx.doi.org/10.4018/ijamc.2022010105.

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Synergistic confluence of pervasive sensing, computing, and networking is generating heterogeneous data at unprecedented scale and complexity. Cloud computing has emergered in the last two decades as a unique storage and computing resource to support a diverse assortment of applications. Numerous organizations are migrating to the cloud to store and process their information. When the cloud infrastructures and resources are insufficient to satisfy end-users requests, scheduling mechanisms are required. Task scheduling, especially in a distributed and heterogeneous system is an NP-hard problem

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CATTAFI, MASSIMILIANO, MARCO GAVANELLI, MADDALENA NONATO, STEFANO ALVISI, and MARCO FRANCHINI. "Optimal placement of valves in a water distribution network with CLP(FD)." Theory and Practice of Logic Programming 11, no. 4-5 (2011): 731–47. http://dx.doi.org/10.1017/s1471068411000275.

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AbstractThis paper presents a new application of logic programming to a real-life problem in hydraulic engineering. The work is developed as a collaboration of computer scientists and hydraulic engineers, and applies Constraint Logic Programming to solve a hard combinatorial problem. This application deals with one aspect of the design of a water distribution network, i.e., the valve isolation system design. We take the formulation of the problem by Giustolisi and Savić (2008 Optimal design of isolation valve system for water distribution networks. InProceedings of the 10th Annual Water Distri

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Juan, Angel Alejandro, Canan Gunes Corlu, Rafael David Tordecilla, Rocio de la Torre, and Albert Ferrer. "On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems." Algorithms 13, no. 1 (2019): 8. http://dx.doi.org/10.3390/a13010008.

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Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard c

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Valouxis, Christos, Christos Gogos, Angelos Dimitsas, Petros Potikas, and Anastasios Vittas. "A Hybrid Exact–Local Search Approach for One-Machine Scheduling with Time-Dependent Capacity." Algorithms 15, no. 12 (2022): 450. http://dx.doi.org/10.3390/a15120450.

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Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we examine the problem of a single machine with time-dependent capacity that performs jobs of deterministic durations, while for each job, its due time is known in advance. The objective is to minimize the aggregated tardiness in all tasks. The problem was motivated by the need to schedule charging times of electric vehicles effectively. We formulate an integer programming model that

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Ahmid, Ali, Thien-My Dao, and Ngan Van Le. "Enhanced Hyper-Cube Framework Ant Colony Optimization for Combinatorial Optimization Problems." Algorithms 14, no. 10 (2021): 286. http://dx.doi.org/10.3390/a14100286.

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Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn them into a set of NP-hard optimization problems. Determining the right optimization algorithm for such problems is a precious point that tends to impact the overall cost of the design process. Furthermore, reinforcing the performance of a prospective optimization algorithm reduces the de

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Vidya Chitre. "Exploring Machine Learning Techniques for Predictive Analytics in Computational Mathematics." Panamerican Mathematical Journal 34, no. 2 (2024): 1–19. http://dx.doi.org/10.52783/pmj.v34.i2.919.

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The machine learning (ML) called predictive analytics has become a useful tool in computer mathematics. It lets you make models that can predict what will happen in the future based on data from the past. This paper looks at several machine learning (ML) methods used in predictive analytics within the field of computational mathematics. It focuses on how they work, what they can be used for, and how well they work. We look at regression analysis, neural networks, decision trees, support vector machines (SVM), and ensemble methods in depth, looking at both their theoretical bases and how they a

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Hammad, Mohamed, Samia Allaoua Chelloug, Reem Alkanhel, et al. "Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model." Sensors 22, no. 17 (2022): 6503. http://dx.doi.org/10.3390/s22176503.

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An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or

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Comakli Sokmen, Özlem, and Mustafa yılmaz. "The new approaches for solving hierarchical Chinese postman problem with stochastic travel times." Journal of Intelligent & Fuzzy Systems, February 28, 2023, 1–22. http://dx.doi.org/10.3233/jifs-222097.

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The hierarchical Chinese postman problem (HCPP) aims to find the shortest tour or tours by passing through the arcs classified according to precedence relationship. HCPP, which has a wide application area in real-life problems such as shovel snow and routing patrol vehicles where precedence relations are important, belongs to the NP-hard problem class. In real-life problems, travel time between the two locations in city traffic varies due to reasons such as traffic jam, weather conditions, etc. Therefore, travel times are uncertain. In this study, HCPP was handled with the chance-constrained s

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Crisan, Gloria Cerasela, Laszlo Barna Iantovics, and László Kovács. "On the neutrality of two symmetric TSP solvers toward instance specification." SCIENCE CHINA Information Sciences 62, no. 219103 (2019). https://doi.org/10.1007/s11432-018-9829-5.

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Currently, many computationally difficult problems can be solved using very efficient methods. Some of these state-of-the-art methods have onlineimplementations. The research question addressed herein is how sensitive are such implementations ifthe input data are preprocessed in a specific manner? The symmetric traveling salesman problem(sTSP), which is an NP-hard problem with many real-life applications is studied. The proposedmethod includes systematic transformation using rotations and reflections of the vertex order ofsTSP instances. This model was used for in

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Gillen, Colin P., Alexander Veremyev, Oleg A. Prokopyev, and Eduardo L. Pasiliao. "Fortification Against Cascade Propagation Under Uncertainty." INFORMS Journal on Computing, March 9, 2021. http://dx.doi.org/10.1287/ijoc.2020.0992.

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Network cascades represent a number of real-life applications: social influence, electrical grid failures, viral spread, and so on. The commonality between these phenomena is that they begin from a set of seed nodes and spread to other regions of the network. We consider a variant of a critical node detection problem dubbed the robust critical node fortification problem, wherein the decision maker wishes to fortify nodes (within a budget) to limit the spread of cascading behavior under uncertain conditions. In particular, the arc weights—how much influence one node has on another in the cascad

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Salemi, Hosseinali, and Austin Buchanan. "Solving the Distance-Based Critical Node Problem." INFORMS Journal on Computing, January 19, 2022. http://dx.doi.org/10.1287/ijoc.2021.1136.

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In critical node problems, the task is to identify a small subset of so-called critical nodes whose deletion maximally degrades a network’s “connectivity” (however that is measured). Problems of this type have been widely studied, for example, for limiting the spread of infectious diseases. However, existing approaches for solving them have typically been limited to networks having fewer than 1,000 nodes. In this paper, we consider a variant of this problem in which the task is to delete b nodes so as to minimize the number of node pairs that remain connected by a path of length at most k. Wit

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Molenbruch, Yves, Kris Braekers, Ohad Eisenhandler, and Mor Kaspi. "The Electric Dial-a-Ride Problem on a Fixed Circuit." Transportation Science, April 25, 2023. http://dx.doi.org/10.1287/trsc.2023.1208.

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Shared mobility services involving electric autonomous shuttles have increasingly been implemented in recent years. Because of various restrictions, these services are currently offered on fixed circuits and operated with fixed schedules. This study introduces a service variant with flexible stopping patterns and schedules. Specifically, in the electric dial-a-ride problem on a fixed circuit (eDARP-FC), a fleet of capacitated electric shuttles operates on a given circuit consisting of a recharging depot and a sequence of stations where passengers can be picked up and dropped off. The shuttles

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Boeckling, Toon, and Antoon Bronselaer. "Cleaning data with Swipe." Journal of Data and Information Quality, February 14, 2025. https://doi.org/10.1145/3712205.

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The repair problem for functional dependencies is the problem where an input database needs to be modified such that all functional dependencies are satisfied and the difference with the original database is minimal. The output database is then called a minimal-cost repair . If the allowed modifications are value updates, finding a minimal-cost repair is \(\mathsf {NP} \) -hard. A well-known approach to find approximations of minimal-cost repairs builds a Chase tree in which each internal node resolves violations of one functional dependency and leaf nodes represent repairs. A key property of

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Jiang, Shan, Shu-Cherng Fang та Qingwei Jin. "Sparse Solutions by a Quadratically Constrained ℓq (0 < q < 1) Minimization Model". INFORMS Journal on Computing, 30 вересня 2020. http://dx.doi.org/10.1287/ijoc.2020.1004.

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Finding sparse solutions to a system of equations and/or inequalities is an important topic in many application areas such as signal processing, statistical regression and nonparametric modeling. Various continuous relaxation models have been proposed and widely studied to deal with the discrete nature of the underlying problem. In this paper, we propose a quadratically constrained [Formula: see text] (0 < q < 1) minimization model for finding sparse solutions to a quadratic system. We prove that solving the proposed model is strongly NP-hard. To tackle the computation difficulty, a firs

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Cicerone, Serafino, and Stefano Gabriele Di. "Special Issue on "Graph Algorithms and Applications"." May 10, 2021. https://doi.org/10.3390/a14050150.

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The mixture of data in real life exhibits structure or connection property in nature. Typical data include biological data, communication network data, image data, etc. Graphs provide a natural way to represent and analyze these types of data and their relationships. For instance, more recently, graphs have found new applications in solving problems for emerging research fields such as social network analysis, design of robust computer network topologies, frequency allocation in wireless networks, and bioinformatics. Unfortunately, the related algorithms usually suffer from high computational

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Maddox, Alexia, and Luke J. Heemsbergen. "Digging in Crypto-Communities’ Future-Making." M/C Journal 24, no. 2 (2021). http://dx.doi.org/10.5204/mcj.2755.

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Introduction This article situates the dark as a liminal and creative space of experimentation where tensions are generative and people tinker with emerging technologies to create alternative futures. Darkness need not mean chaos and fear of violence – it can mean privacy and protection. We define dark as an experimental space based upon uncertainties rather than computational knowns (Bridle) and then demonstrate via a case study of cryptocurrencies the contribution of dark and liminal social spaces to future(s)-making. Cryptocurrencies are digital cash systems that use decentralised (peer-to-

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Lackner, Marie-Louise, Christoph Mrkvicka, Nysret Musliu, Daniel Walkiewicz, and Felix Winter. "Exact methods for the Oven Scheduling Problem." Constraints, July 4, 2023. http://dx.doi.org/10.1007/s10601-023-09347-2.

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AbstractThe Oven Scheduling Problem (OSP) is a new parallel batch scheduling problem that arises in the area of electronic component manufacturing. Jobs need to be scheduled to one of several ovens and may be processed simultaneously in one batch if they have compatible requirements. The scheduling of jobs must respect several constraints concerning eligibility and availability of ovens, release dates of jobs, setup times between batches as well as oven capacities. Running the ovens is highly energy-intensive and thus the main objective, besides finishing jobs on time, is to minimize the cumul

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Sara, Salah1 Abdel-Rahman Hedar2 and Marghny. H. Mohammed3. "ENHANCED POPULATION BASED ANT COLONY FOR THE 3D HYDROPHOBIC POLAR PROTEIN STRUCTURE PREDICTION PROBLEM." September 23, 2013. https://doi.org/10.5281/zenodo.1402500.

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International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013 DOI: 10.5121/ijbb.2013.3304 41 ENHANCED POPULATION BASED ANT COLONY FOR THE 3D HYDROPHOBIC POLAR PROTEIN STRUCTURE PREDICTION PROBLEM Sara Salah1 , Abdel-Rahman Hedar2 and Marghny. H. Mohammed3 1 Computer Science Department, Faculty of Science, Assiut University 2 Computer Science Department, Faculty of Computers and Information, Assiut University 3Computer Science Department, Faculty of Computers and Information, Assiut University ABSTRACT Population-based Ant Colony algorithm is stochastic local sear

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Behnia, Bardia, Babak Shirazi, Iraj Mahdavi, and Mohammad Mahdi Paydar. "Nested bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest." RAIRO - Operations Research, August 29, 2019. http://dx.doi.org/10.1051/ro/2019075.

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Due to the competitive nature of the market and the various products production requirements with short life cycles, cellular manufacturing systems have found a special role in manufacturing environments. Creativity and innovation in products are the results of the mental effort of the workforces in addition to machinery and parts allocation. Assignment of the workforce to cells based on the interest and ability indices is a tactical decision while the cell formation is a strategic decision. To make the correct decision, these two problems should be solved separately while considering their im

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Khan, Sulaiman, Habib Ullah Khan, and Shah Nazir. "Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-26090-5.

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AbstractBig data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors

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Shaw, Janice Marion. "The Curious Transformation of Boy to Computer." M/C Journal 19, no. 4 (2016). http://dx.doi.org/10.5204/mcj.1130.

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Mark Haddon’s The Curious Incident of the Dog in the Night-Time has achieved success as “the new Rain Man” or “the new definitive, popular account of the autistic condition” (Burks-Abbott 294). Integral to its favourable reception is the way it conflates the autistic main character, the fifteen-year-old narrator Christopher Boone, with the savant, or individual who exhibits both neurological problems and giftedness, thereby engaging with the way autism is presented in popular culture. In a variety of contemporary films and television series, autism has been transformed from a disability to a f

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Dieter, Michael. "Amazon Noir." M/C Journal 10, no. 5 (2007). http://dx.doi.org/10.5204/mcj.2709.

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   There is no diagram that does not also include, besides the points it connects up, certain relatively free or unbounded points, points of creativity, change and resistance, and it is perhaps with these that we ought to begin in order to understand the whole picture. (Deleuze, “Foucault” 37) Monty Cantsin: Why do we use a pervert software robot to exploit our collective consensual mind? Letitia: Because we want the thief to be a digital entity. Monty Cantsin: But isn’t this really blasphemic? Letitia: Yes, but god – in our case a meta-cocktail of authorship and copyright –

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