Bibliografias temáticas / Competitive algorithms

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Literatura científica selecionada sobre o tema "Competitive algorithms"

Autor: Grafiati

Publicado: 25 de maio de 2024

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Artigos de revistas sobre o assunto "Competitive algorithms"

Im, Sungjin, Janardhan Kulkarni, and Kamesh Munagala. "Competitive Algorithms from Competitive Equilibria." Journal of the ACM 65, no. 1 (2018): 1–33. http://dx.doi.org/10.1145/3136754.

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Bender, Michael A., Jeremy T. Fineman, Mahnush Movahedi, et al. "Resource-Competitive Algorithms." ACM SIGACT News 46, no. 3 (2015): 57–71. http://dx.doi.org/10.1145/2818936.2818949.

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Fiat, Amos, Richard M. Karp, Michael Luby, Lyle A. McGeoch, Daniel D. Sleator, and Neal E. Young. "Competitive paging algorithms." Journal of Algorithms 12, no. 4 (1991): 685–99. http://dx.doi.org/10.1016/0196-6774(91)90041-v.

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Budura, Georgeta, Corina Botoca, and Nicolae Miclău. "Competitive learning algorithms for data clustering." Facta universitatis - series: Electronics and Energetics 19, no. 2 (2006): 261–69. http://dx.doi.org/10.2298/fuee0602261b.

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This paper presents and discusses some competitive learning algorithms for data clustering. A new competitive learning algorithm, named the dynamically penalized rival competitive learning algorithm (DPRCL), is introduced and studied. It is a variant of the rival penalized competitive algorithm [1] and it performs appropriate clustering without knowing the clusters number, by automatically driving the extra seed points far away from the input data set. It does not have the 'dead units' problem. Simulations results, performed in different conditions, are presented showing that the performance of the new DPRCL algorithm is better comparative with other competitive algorithms.

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LIU, ZHI-QIANG, and YAJUN ZHANG. "COMPENSATION COMPETITIVE LEARNING." International Journal of Computational Intelligence and Applications 01, no. 03 (2001): 303–22. http://dx.doi.org/10.1142/s1469026801000263.

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In general, in competitive learning the requirement for the initial number of prototypes is a difficult task, as we do not usually know the number of clusters in the input data a priori. The behavior and performance of the competitive algorithms are very sensitive to the initial locations and number of the prototypes. In this paper after investigating several important competitive learning paradigms, we present compensation techniques for overcoming the problems in competitive learning. Our experimental results show that competition with compensation can improve the performance of the learning algorithm.

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Fiat, Amos, Yuval Rabani, and Yiftach Ravid. "Competitive k-server algorithms." Journal of Computer and System Sciences 48, no. 3 (1994): 410–28. http://dx.doi.org/10.1016/s0022-0000(05)80060-1.

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Majd, Amin, Golnaz Sahebi, Masoud Daneshtalab, Juha Plosila, Shahriar Lotfi, and Hannu Tenhunen. "Parallel imperialist competitive algorithms." Concurrency and Computation: Practice and Experience 30, no. 7 (2018): e4393. http://dx.doi.org/10.1002/cpe.4393.

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Wu, Yonghua, Guohun Zhu, Huaying Chen, and Jucun Qin. "WIN Algorithm for Discrete Online TSP." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1199–202. http://dx.doi.org/10.20965/jaciii.2011.p1199.

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Traveling Salesman Problem (TSP) which is proved as an NP-Complete problem is solved by many algorithms. In this paper, we propose online TSP which is based on general discrete metric space. A Waiting-If-Necessary (WIN) algorithm is proposed that involves with increasing cost caused by zealous algorithms and unnecessary waiting caused by cautious algorithms. We measure the performance of the WIN algorithm using competitive analysis and found that it is a 2-competitive algorithm. The competitive ratio of theWIN algorithm can be improved by setting parameterT0.

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Osman, Hossam, and Moustafa M. Fahmy. "Probabilistic Winner-Take-All Learning Algorithm for Radial-Basis-Function Neural Classifiers." Neural Computation 6, no. 5 (1994): 927–43. http://dx.doi.org/10.1162/neco.1994.6.5.927.

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This paper proposes a new adaptive competitive learning algorithm called “the probabilistic winner-take-all.” The algorithm is based on a learning scheme developed by Agrawala within the statistical pattern recognition literature (Agrawala 1970). Its name stems from the fact that for a given input pattern once each competitor computes the probability of being the one that generated this pattern, the computed probabilities are utilized to probabilistically choose a winner. Then, only this winner is permitted to learn. The learning rule of the algorithm is derived for three different cases. Its properties are discussed and compared to those of two other competitive learning algorithms, namely the standard winner-take-all and the maximum-likelihood soft competition. Experimental comparison is also given. When all three algorithms are used to train the hidden layer of radial-basis-function classifiers, experiments indicate that classifiers trained with the probabilistic winner-take-all outperform those trained with the other two algorithms.

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Mohapatra, Prabhujit, Kedar Nath Das, Santanu Roy, Ram Kumar, and Nilanjan Dey. "A Novel Multi-Objective Competitive Swarm Optimization Algorithm." International Journal of Applied Metaheuristic Computing 11, no. 4 (2020): 114–29. http://dx.doi.org/10.4018/ijamc.2020100106.

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In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.

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Teses / dissertações sobre o assunto "Competitive algorithms"

Li, Rongbin, and 李榕滨. "New competitive algorithms for online job scheduling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/197555.

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Job scheduling, which greatly impacts on the system performance, is a fundamental problem in computer science. In this thesis, we study three kinds of scheduling problems, that is, deadline scheduling, due date scheduling, and flow time scheduling. Traditionally, the major concern for scheduling problems is the system performance, i.e. the “Quality of Service" (QoS). Different scheduling problems use different QoS measurements. For deadline scheduling, the most common QoS to optimize is the throughput; for due date scheduling, it is the total quoted lead time; and for flow time scheduling, it is the total (weighted) flow time. Recently, energy efficiency is becoming more and more important. Many modern processors adopt technologies like dynamic speed scaling and sleep management to reduce energy usage. Much work is done on energy efficient scheduling. In this thesis, we study this topic for all three kinds of scheduling mentioned above. Meanwhile, we also revisit the traditional flow time scheduling problem to optimize the QoS. However, we consider the problem in a more realistic model that makes the problem much more challenging. Below is the summary of the problems studied in the thesis. First, we consider the tradeoff between energy and throughput for deadline scheduling. Specifically, each job is associated with a value (or importance) and a deadline. A scheduling algorithm is allowed to discard some of the jobs, and the objective is to minimize total energy usage plus total value of discarded jobs. When processor's maximum speed is unbounded, we propose an O(1)-competitive algorithm. When processor's maximum speed is bounded, we show a strong lower bound and give an algorithm with a competitive ratio close to that lower bound. Second, we study energy efficient due date scheduling. Jobs arrive online with different sizes and weights. An algorithm needs to assign a due date to each job once it arrives, and complete the job by the due date. The quoted lead time of a job equals its due date minus its arrival time, multiplied by its weight. We propose a competitive algorithm for minimizing the sum of the total quoted lead time and energy usage. Next, we consider flow time scheduling with power management on multiple machines. Jobs with arbitrary sizes and weights arrive online. Each machine consumes different amount of energy when processing a job, idling or sleeping. A scheduler has to maintain a good balance of the states of the machines to avoid energy wastage and, meanwhile, guarantee high QoS. Our result is an O(1)-competitive algorithm to minimize total weighted flow time plus energy usage. Finally, we consider the traditional preemptive scheduling to minimize total flow time. Previous theoretical results often assume preemption is free, which is not true for most systems. We investigate the complexity of the problem when a processor has to perform a certain amount of overhead before it resumes the execution of a job preempted before. We first show an Ω(n^(1/4)) lower bound, and then, propose a (1+ε)-speed (1+ 1/ε )-competitive algorithm in resource augmentation model. published_or_final_version Computer Science Doctoral Doctor of Philosophy

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Wong, Chiu Wai M. Eng Massachusetts Institute of Technology. "Competitive algorithms for online matching and vertex cover problems." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85521.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (pages 73-75). The past decade has witnessed an explosion of research on the online bipartite matching problem. Surprisingly, its dual problem, online bipartite vertex cover, has never been explicitly studied before. One of the motivation for studying this problem is that it significantly generalizes the classical ski rental problem. An instance of such problems specifies a bipartite graph G = (L, R, E) whose left vertices L are offline and right vertices arrive online one at a time. An algorithm must maintain a valid vertex cover from which no vertex can ever be removed. The objective is to minimize the size of the cover. In this thesis, we introduce a charging-based algorithmic framework for this problem as well as its generalizations. One immediate outcome is a simple analysis of an optimal 1/1-1/e- competitive algorithm for online bipartite vertex cover. By extending the charging-based analysis in various nontrivial ways, we also obtain optimal l_1 e-competitive algorithms for the edge-weighted and submodular versions of online bipartite vertex cover, which all match the best performance of ski rental. As an application, we show that by analyzing our algorithm in the primal-dual framework, our result on submodular vertex cover implies an optimal (1/1-1/e)-competitive algorithm for its dual, online bipartite submodular matching. This problem is a generalization of online bipartite matching and may have applications in display ad allocation. We consider also the more general scenario where all the vertices are online and the graph is not necessarily bipartite, which is known as the online fractional vertex cover and matching problems. Our contribution in this direction is a primal-dual 1.901-competitive (or 1/1.901 ~~ 0.526) algorithm for these problems. Previously, it was only known that they admit a simple well-known 2-competitive (or 1/2) greedy algorithm. Our result is the first successful attempt to beat the greedy algorithm for these two problems. Moreover, our algorithm for the online matching problem significantly generalizes the traditional online bipartite graph matching problem, where vertices from only one side of the bipartite graph arrive online. In particular, our algorithm improves upon the result of the fractional version of the online edge-selection problem in Blum et. al. (JACM '06). Finally, on the hardness side, we show that no randomized online algorithm can achieve a competitive ratio better than 1.753 and 0.625 for the online fractional vertex cover problem and the online fractional matching problem respectively, even for bipartite graphs. by Chiu Wai Wong. M. Eng.

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Chan, Sze-hang, and 陳思行. "Competitive online job scheduling algorithms under different energy management models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/206690.

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Online flow-time scheduling is a fundamental problem in computer science and has been extensively studied for years. It is about how to design a scheduler to serve computer jobs with unpredictable arrival times and varying sizes and priorities so as to minimize the total flow time (better understood as response time) of jobs. It has many applications, most notable in the operating of server farms. As energy has become an important issue, the design of scheduler also has to take power management into consideration, for example, how to scale the speed of the processors dynamically. The objectives are orthogonal as one would prefer lower processor speed to save energy, yet a good quality of service must be retained. In this thesis, I study a few scheduling problems for energy and flow time in depth and give new algorithms to tackle them. The competitiveness of our algorithms is guaranteed with worst-case mathematical analysis against the best possible or hypothetical solutions. In the speed scaling model, the power of a processor increases with its speed according to a certain function (e.g., a cubic function of speed). Among all online scheduling problems with speed scaling, the nonclairvoyant setting (in which the size of a job is not known during its execution) with arbitrary priorities is perhaps the most challenging. This thesis gives the first competitive algorithm called WLAPS for this setting. In reality, it is not uncommon that during the peak-load period, some (low-priority) users have their jobs rejected by the servers. This triggers me to study more complicated scheduling algorithms that can strike a good balance among speed scaling, flow time and rejection penalty. Two new algorithms UPUW and HDFAC for different models of rejection penalty have been proposed and analyzed. Last, but perhaps the most interesting, we study power management in large server farm environment in which the primary energy saving mechanism is to put some processors to sleep. Two new algorithms POOL and SATA have been designed to tackle jobs that cannot and can migrate among the processors, respectively. They are integrated algorithms that can consider speed scaling, job scheduling and processor sleep management together to optimize the energy usage and ow time simultaneously. These algorithms are again proven mathematically to be competitive even in the worst case. published_or_final_version Computer Science Doctoral Doctor of Philosophy

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McNeill, Dean K. "Adaptive visual representations for autonomous mobile robots using competitive learning algorithms." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ35045.pdf.

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Zhang, Kening. "A COMPETITIVE RECONFIGURATION APPROACH TO AUTONOMOUS FAULT HANDLING USING GENETIC ALGORITHMS." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2319.

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In this dissertation, a novel self-repair approach based on Consensus Based Evaluation (CBE) for autonomous repair of SRAM-based Field Programmable Gate Arrays (FPGAs) is developed, evaluated, and refined. An initial population of functionally identical (same input-output behavior), yet physically distinct (alternative design or place-and-route realization) FPGA configurations is produced at design time. During run-time, the CBE approach ranks these alternative configurations after evaluating their discrepancy relative to the consensus formed by the population. Through runtime competition, faults in the logical resources become occluded from the visibility of subsequent FPGA operations. Meanwhile, offspring formed through crossover and mutation of faulty and viable configurations are selected at a controlled re-introduction rate for evaluation and refurbishment. Refurbishments are evolved in-situ, with online real-time input-based performance evaluation, enhancing system availability and sustainability, creating an Organic Embedded System (OES). A fault tolerance model called N Modular Redundancy with Standby (NMRSB) is developed which combines the two popular fault tolerance techniques of NMR and Standby fault tolerance in order to facilitate the CBE approach. This dissertation develops two of instances of the NMRSB system Triple Modular Redundancy with Standby (TMRSB) and Duplex with Standby (DSB). A hypothetical Xilinx Virtex-II Pro FPGA model demonstrates their viability for various applications including a 3-bit x 3-bit multiplier, and the MCNC91 benchmark circuits. Experiments conducted on the model iii evaluate the performance of three new genetic operators and demonstrate progress towards a completely self-contained single-chip implementation so that the FPGA can refurbish itself without requiring a PC host to execute the Genetic Algorithm. This dissertation presents results from the simulations of multiple applications with a CBE model implemented in the C++ programming language. Starting with an initial population of 20 and 30 viable configurations for TMRSB and DSB respectively, a single stuck-at fault is introduced in the logic resources. Fault refurbishment experiments are conducted under supervision of CBE using a fitness state evaluation function based on competing outputs, fitness adjustment, and different level threshold. The device remains online throughout the process by which a complete repair is realized with Hamming Distance and Bitweight voting schemes. The results indicate a Hamming Distance TMRSB approach can prevent the most pervasive fault impacts and realize complete refurbishment. Experimental results also show that the Autonomic Layer demonstrates 100% faulty component isolation for both Functional Elements (FEs) and Autonomous Elements (AEs) with randomly injected single and multiple faults. Using logic circuits from the MCNC-91 benchmark set, availability during repair phases averaged 75.05%, 82.21%, and 65.21% for the z4ml, cm85a, and cm138a circuits respectively under stated conditions. In addition to simulation, the proposed OES architecture synthesized from HDL was prototyped on a Xilinx Virtex II Pro FPGA device supporting partial reconfiguration to demonstrate the feasibility for intrinsic regeneration of the selected circuit. Ph.D. School of Electrical Engineering and Computer Science Engineering and Computer Science Computer Engineering PhD

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Premkumar, Aravind Preshant. "Competitive Algorithms and System for Multi-Robot Exploration of Unknown Environments." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78847.

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We present an algorithm to explore an orthogonal polygon using a team of p robots. This algorithm combines ideas from information-theoretic exploration algorithms and computational geometry based exploration algorithms. The algorithm is based on a single-robot polygon exploration algorithm and a tree exploration algorithm. We show that the exploration time of our algorithm is competitive (as a function of p) with respect to the offline optimal exploration algorithm. We discuss how this strategy can be adapted to real-world settings to deal with noisy sensors. In addition to theoretical analysis, we investigate the performance of our algorithm through simulations for multiple robots and experiments with a single robot. Master of Science

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Lorenz, Julian Michael. "Optimal trading algorithms : portfolio transactions, multiperiod portfolio selection, and competitive online search /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17746.

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Liu, Ming. "Design and Evaluation of Algorithms for Online Machine Scheduling Problems." Phd thesis, Ecole Centrale Paris, 2009. http://tel.archives-ouvertes.fr/tel-00453316.

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Dans cette thèse, nous proposons et évaluons des algorithmes pour résoudre des problèmes d'ordonnancement en ligne. Pendant des décennies, les études en ordonnancement considèrent des modèles déterministes où toutes les informations nécessaires pour la définition du problème sont supposées connues à l'avance. Cette hypothèse n'est généralement pas réaliste. Ceci a motivé les études sur l'ordonnancement en ligne. Dans un problème d'ordonnancement en ligne, un algorithme doit prendre des décisions sans connaissance du futur. L'analyse compétitive est généralement la méthode utilisée pour évaluer les performances de tels algorithmes. Dans cette analyse, la performance d'un algorithme en ligne est mesurée par le ratio compétitif qui est le ratio dans le pire cas entre la performance de la solution obtenue et celle d'une solution optimale hors ligne. Nous considérons principalement deux paradigmes en ligne: celui où les tâches se présentent dans la liste et celui où les tâches arrivent au fur et à mesure. Sur la base de ces deux paradigmes, nous considérons différents modèles : une seule machine, deux machines identiques parallèles, deux machines uniformes parallèles, batch machines et open shop. Pour chacun des problèmes, nous démontrons une borne inférieure de ratios compétitifs et proposons des algorithmes en ligne. Ensuite, nous évaluons la performance de ces algorithmes à l'aide de l'analyse compétitive. Pour certains problèmes, nous montrons que les algorithmes proposés sont optimaux dans le sens où le ratio compétitif est égal à la borne inférieure.

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Tsai, Carol Leanne. "Heuristic Algorithms for Agnostically Identifying the Globally Stable and Competitive Metastable Morphologies of Block Copolymer Melts." Thesis, University of California, Santa Barbara, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13423067.

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Block copolymers are composed of chemically distinct polymer chains that can be covalently linked in a variety of sequences and architectures. They are ubiquitous as ingredients of consumer products and also have applications in advanced plastics, drug delivery, advanced membranes, and next generation nano-lithographic patterning. The wide spectrum of possible block copolymer applications is a consequence of block copolymer self-assembly into periodic, meso-scale morphologies as a function of varying block composition and architecture in both melt and solution states, and the broad spectrum of physical properties that such mesophases afford. Materials exploration and discovery has traditionally been pursued through an iterative process between experimental and theoretical/computational collaborations. This process is often implemented in a trial-and-error fashion, and from the computational perspective of generating phase diagrams, usually requires some existing knowledge about the competitive phases for a given system. Self-Consistent Field Theory (SCFT) simulations have proven to be both qualitatively and quantitatively accurate in the determination, or forward mapping, of block copolymer phases of a given system. However, it is possible to miss candidates. This is because SCFT simulations are highly dependent on their initial configurations, and the ability to map phase diagrams requires a priori knowledge of what the competing candidate morphologies are. The unguided search for the stable phase of a block copolymer of a given composition and architecture is a problem of global optimization. SCFT by itself is a local optimization method, so we can combine it with population-based heuristic algorithms geared at global optimization to facilitate forward mapping. In this dissertation, we discuss the development of two such methods: Genetic Algorithm + SCFT (GA-SCFT) and Particle Swarm Optimization + SCFT (PSO-SCFT). Both methods allow a population of configurations to explore the space associated with the numerous states accessible to a block copolymer of a given composition and architecture. GA-SCFT is a real-space method in which a population of SCFT field configurations “evolves” over time. This is achieved by initializing the population randomly, allowing the configurations to relax to local basins of attraction using SCFT simulations, then selecting fit members (lower free energy structures) to recombine their fields and undergo mutations to generate a new “generation” of structures that iterate through this process. We present results from benchmark testing of this GA-SCFT technique on the canonical AB diblock copolymer melt, for which the theoretical phase diagram has long been established. The GA-SCFT algorithm successfully predicts many of the conventional mesophases from random initial conditions in large, 3-dimensional simulation cells, including hexagonally-packed cylinders, BCC-packed spheres, and lamellae, over a broad composition range and weak to moderate segregation strength. However, the GA-SCFT method is currently not effective at discovery of network phases, such as the Double-Gyroid (GYR) structure. PSO-SCFT is a reciprocal space approach in which Fourier components of SCFT fields near the principal shell are manipulated. Effectively, PSO-SCFT facilitates the search through a space of reciprocal-space SCFT seeds which yield a variety of morphologies. Using intensive free energy as a fitness metric by which to compare these morphologies, the PSO-SCFT methodology allows us to agnostically identify low-lying competitive and stable morphologies. We present results for applying PSO-SCFT to conformationally symmetric diblock copolymers and a miktoarm star polymer, AB4, which offers a rich variety of competing sphere structures. Unlike the GA-SCFT method we previously presented, PSO-SCFT successfully predicts the double gyroid morphology in the AB-diblock. Furthermore, PSO-SCFT successfully recovers the A 15 morphology at a composition where it is expected to be stable in the miktoarm system, as well as several competitive metastable candidates, and a new sphere morphology belonging to the hexagonal space group 191, which has not been seen before in polymer systems. Thus, we believe the PSO-SCFT method provides a promising platform for screening for competitive structures in a given block copolymer system.

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Nayyar, Krati. "Input Sensitive Analysis of a Minimum Metric Bipartite Matching Algorithm." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/86518.

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In various business and military settings, there is an expectation of on-demand delivery of supplies and services. Typically, several delivery vehicles (also called servers) carry these supplies. Requests arrive one at a time and when a request arrives, a server is assigned to this request at a cost that is proportional to the distance between the server and the request. Bad assignments will not only lead to larger costs but will also create bottlenecks by increasing delivery time. There is, therefore, a need to design decision-making algorithms that produce cost-effective assignments of servers to requests in real-time. In this thesis, we consider the online bipartite matching problem where each server can serve exactly one request. In the online minimum metric bipartite matching problem, we are provided with a set of server locations in a metric space. Requests arrive one at a time that have to be immediately and irrevocably matched to a free server. The total cost of matching all the requests to servers, also known as the online matching is the sum of the cost of all the edges in the matching. There are many well-studied models for request generation. We study the problem in the adversarial model where an adversary who knows the decisions made by the algorithm generates a request sequence to maximize ratio of the cost of the online matching and the minimum-cost matching (also called the competitive ratio). An algorithm is a-competitive if the cost of online matching is at most 'a' times the minimum cost. A recently discovered robust and deterministic online algorithm (we refer to this as the robust matching or the RM-Algorithm) was shown to have optimal competitive ratios in the adversarial model and a relatively weaker random arrival model. We extend the analysis of the RM-Algorithm in the adversarial model and show that the competitive ratio of the algorithm is sensitive to the input, i.e., for "nice" input metric spaces or "nice" server placements, the performance guarantees of the RM-Algorithm is significantly better. In fact, we show that the performance is almost optimal for any fixed metric space and server locations. Master of Science

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Livros sobre o assunto "Competitive algorithms"

Kothari, Ravi. A competitive genetic algorithm for single row facility layout. Indian Institute of Management, 2012.

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Borodin, Allan. Online computation and competitive analysis. Cambridge University Press, 1998.

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Kravets, O. Ya. Information systems competitive development: Algorithms and software for intranet interfaces. Science Book Publishing House, 2013.

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Koren, Gilad. An optimal scheduling algorithm with a competitive factor for real-time systems. Courant Institute of Mathematical Sciences, New York University, 1991.

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Koren, Gilad. An optimal scheduling algorithm with a competitive factor for real-time systems. Courant Institute of Mathematical Sciences, New York University, 1991.

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Valdez, Fevrier, Juan Barraza, and Patricia Melin. Hybrid Competitive Learning Method Using the Fireworks Algorithm and Artificial Neural Networks. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47712-6.

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Online Computation and Competitive Analysis. Cambridge University Press, 2005.

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Laaksonen, Antti. Guide to Competitive Programming: Learning and Improving Algorithms Through Contests. Springer, 2020.

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Buchbinder, Niv, and Joseph (Seffi) Naor. Design of Competitive Online Algorithms Via a Primal-Dual Approach. Now Publishers, 2009.

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Competitive Programming in Python: 128 Algorithms to Develop Your Coding Skills. University of Cambridge ESOL Examinations, 2020.

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Capítulos de livros sobre o assunto "Competitive algorithms"

Bu, Tian-Ming. "Competitive Auction." In Encyclopedia of Algorithms. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_78.

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Bu, Tian-Ming. "Competitive Auction." In Encyclopedia of Algorithms. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_78-2.

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Bu, Tian-Ming. "Competitive Auction." In Encyclopedia of Algorithms. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_78.

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Fiat, Amos, and Gerhard J. Woeginger. "Competitive analysis of algorithms." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029562.

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Irani, Sandy. "Competitive analysis of paging." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029564.

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Fiat, Amos, and Gerhard J. Woeginger. "Competitive odds and ends." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029578.

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Aspnes, James. "Competitive analysis of distributed algorithms." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029567.

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Deshmukh, Kaustubh, Andrew V. Goldberg, Jason D. Hartline, and Anna R. Karlin. "Truthful and Competitive Double Auctions." In Algorithms — ESA 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45749-6_34.

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Bose, Prosenjit, Rolf Fagerberg, André van Renssen, and Sander Verdonschot. "Competitive Local Routing with Constraints." In Algorithms and Computation. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48971-0_3.

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Harks, Tobias, Stefan Heinz, and Marc E. Pfetsch. "Competitive Online Multicommodity Routing." In Approximation and Online Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/11970125_19.

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Trabalhos de conferências sobre o assunto "Competitive algorithms"

Im, Sungjin, Janardhan Kulkarni, and Kamesh Munagala. "Competitive algorithms from competitive equilibria." In STOC '14: Symposium on Theory of Computing. ACM, 2014. http://dx.doi.org/10.1145/2591796.2591814.

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Paiton, Dylan M., Steven Shepard, Kwan Ho Ryan Chan, and Bruno A. Olshausen. "Subspace Locally Competitive Algorithms." In NICE '20: Neuro-inspired Computational Elements Workshop. ACM, 2020. http://dx.doi.org/10.1145/3381755.3381765.

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Coester, Christian, Roie Levin, Joseph (Seffi) Naor, and Ohad Talmon. "Competitive Algorithms for Block-Aware Caching." In SPAA '22: 34th ACM Symposium on Parallelism in Algorithms and Architectures. ACM, 2022. http://dx.doi.org/10.1145/3490148.3538567.

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Kamali, Shahin, and Helen Xu. "Multicore Paging Algorithms Cannot Be Competitive." In SPAA '20: 32nd ACM Symposium on Parallelism in Algorithms and Architectures. ACM, 2020. http://dx.doi.org/10.1145/3350755.3400270.

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Buchbinder, Niv, Shahar Chen, and Joseph (Seffi) Naor. "Competitive Analysis via Regularization." In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973402.32.

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Plyasunov, A., and A. Panin. "On three-level problem of competitive pricing." In NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965327.

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Manasse, Mark, Lyle McGeoch, and Daniel Sleator. "Competitive algorithms for on-line problems." In the twentieth annual ACM symposium. ACM Press, 1988. http://dx.doi.org/10.1145/62212.62243.

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Rozell, Christopher, Don Johnson, Richard Baraniuk, and Bruno Olshausen. "Locally Competitive Algorithms for Sparse Approximation." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379981.

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Jinhong Xu, Weijun Xu, Jinling Li, and Yucheng Dong. "Competitive Algorithms about Online Reverse Auctions." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631185.

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Liu, Hui, and David Y. Yun. "Competitive learning algorithms for image coding." In Aerospace Sensing, edited by Steven K. Rogers. SPIE, 1992. http://dx.doi.org/10.1117/12.140018.

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Relatórios de organizações sobre o assunto "Competitive algorithms"

Marty, Frédéric, and Thierry Warin. Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement. CIRANO, 2023. http://dx.doi.org/10.54932/iwpg7510.

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This paper examines algorithmic collusion from legal and economic perspectives, highlighting the growing role of algorithms in digital markets and their potential for anti-competitive behavior. Using bandit algorithms as a model, traditionally applied in uncertain decision-making contexts, we illuminate the dynamics of implicit collusion without overt communication. Legally, the challenge is discerning and classifying these algorithmic signals, especially as unilateral communications. Economically, distinguishing between rational pricing and collusive patterns becomes intricate with algorithm-driven decisions. The paper emphasizes the imperative for competition authorities to identify unusual market behaviors, hinting at shifting the burden of proof to firms with algorithmic pricing. Balancing algorithmic transparency and collusion prevention is crucial. While regulations might address these concerns, they could hinder algorithmic development. As this form of collusion becomes central in antitrust, understanding through models like bandit algorithms is vital, since these last ones may converge faster towards an anticompetitive equilibrium.

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Brown, Zach, and Alexander MacKay. Competition in Pricing Algorithms. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w28860.

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Iyer, Ganesh, and T. Tony Ke. Competitive Model Selection in Algorithmic Targeting. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w31002.

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Parker, Robert, and Carleton Coffrin. The Grid Optimization Competition Benchmark Algorithm. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2202592.

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Chang, Shu-jen, Ray Perlner, William E. Burr, et al. Third-Round Report of the SHA-3 Cryptographic Hash Algorithm Competition. National Institute of Standards and Technology, 2012. http://dx.doi.org/10.6028/nist.ir.7896.

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Regenscheid, Andrew, Ray Perlner, Shu-jen Chang, John Kelsey, Mridul Nandi, and Souradyuti Paul. Status report on the first round of the SHA-3 cryptographic hash algorithm competition. National Institute of Standards and Technology, 2009. http://dx.doi.org/10.6028/nist.ir.7620.

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Turan, Meltem Sonmez, Ray Perlner, Lawrence E. Bassham, et al. Status report on the second round of the SHA-3 cryptographic hash algorithm competition. National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7764.

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Elacqua, Gregory, Anne Sofie Westh Olsen, and Santiago Velez-Ferro. Open configuration options The Market Design Approach to Teacher Assignment: Evidence from Ecuador. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003824.

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We study the advantages, trade-offs, and challenges of employing a centralized rule to determine the allocation of teachers to schools. Data come from the centralized teacher assignment program in Ecuador, "Quiero ser Maestro," conducted by the Ministry of Education. Notably, in 2019 the program transitioned from a priority based algorithm to a strategy proof mechanism, similar to the change introduced in Boston in 2005 to assign students to schools. Using the reported preferences, we conduct a counterfactual analysis and nd substantive evidence that the adjustment in algorithm resulted in greater efficiency for the school system. However, in contrast to the Boston case, we nd the benefits stem from increasing the competition for positions among teachers, rather than by the introduction of a strategy-proof mechanism.

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Durovic, Mateja, and Franciszek Lech. A Consumer Law Perspective on the Commercialization of Data. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64577.

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Commercialization of consumers’ personal data in the digital economy poses serious, both conceptual and practical, challenges to the traditional approach of European Union (EU) Consumer Law. This article argues that mass-spread, automated, algorithmic decision-making casts doubt on the foundational paradigm of EU consumer law: consent and autonomy. Moreover, it poses threats of discrimination and under- mining of consumer privacy. It is argued that the recent legislative reaction by the EU Commission, in the form of the ‘New Deal for Consumers’, was a step in the right direction, but fell short due to its continued reliance on consent, autonomy and failure to adequately protect consumers from indirect discrimination. It is posited that a focus on creating a contracting landscape where the consumer may be properly informed in material respects is required, which in turn necessitates blending the approaches of competition, consumer protection and data protection laws.

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Seginer, Ido, James Jones, Per-Olof Gutman, and Eduardo Vallejos. Optimal Environmental Control for Indeterminate Greenhouse Crops. United States Department of Agriculture, 1997. http://dx.doi.org/10.32747/1997.7613034.bard.

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Increased world competition, as well as increased concern for the environment, drive all manufacturing systems, including greenhouses, towards high-precision operation. Optimal control is an important tool to achieve this goal, since it finds the best compromise between conflicting demands, such as higher profits and environmental concerns. The report, which is a collection of papers, each with its own abstract, outlines an approach for optimal, model-based control of the greenhouse environment. A reliable crop model is essential for this approach and a significant portion of the effort went in this direction, resulting in a radically new version of the tomato model TOMGRO, which can be used as a prototype model for other greenhouse crops. Truly optimal control of a very complex system requires prohibitively large computer resources. Two routes to model simplification have, therefore, been tried: Model reduction (to fewer state variables) and simplified decision making. Crop model reduction from nearly 70 state variables to about 5, was accomplished by either selecting a subset of the original variables or by forming combinations of them. Model dynamics were then fitted either with mechanistic relationships or with neural networks. To simplify the decision making process, the number of costate variables (control policy parametrs) was recuced to one or two. The dry-matter state variable was transformed in such a way that its costate became essentially constant throughout the season. A quasi-steady-state control algorithm was implemented in an experimental greenhouse. A constant value for the dry-matter costate was able to control simultaneously ventilation and CO2 enrichment by continuously producing weather-dependent optimal setpoints and then maintaining them closely.

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