Relevant bibliographies by topics / Online algorithms with recourse

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Online algorithms with recourse'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Online algorithms with recourse"

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Vasilopoulos, Vasileios, Georgios Pavlakos, Karl Schmeckpeper, Kostas Daniilidis, and Daniel E. Koditschek. "Reactive navigation in partially familiar planar environments using semantic perceptual feedback." International Journal of Robotics Research 41, no. 1 (2021): 85–126. http://dx.doi.org/10.1177/02783649211048931.

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This article solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in simultaneous localization and mapping (SLAM) and visual object recognition to recast prior geometric knowledge in terms of an offline catalog of familiar objects. The resulting vector field planner guarantees convergence to an arbitrarily specified goal, avoiding collisions along the way with fixed but arbitrarily placed instances from the catalog as well as completely unknown fixed obstacles so long as they are strongly convex and well separated. We illustrate the generic robustness properties of such deterministic reactive planners as well as the relatively modest computational cost of this algorithm by supplementing an extensive numerical study with physical implementation on both a wheeled and legged platform in different settings.
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Abdelkader, Krifa, and Bouzrara Kais. "Robust H∞ gain neuro-adaptive observer design for nonlinear uncertain systems." Transactions of the Institute of Measurement and Control 41, no. 8 (2018): 2293–309. http://dx.doi.org/10.1177/0142331218798685.

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To guarantee convergent state estimates and exact approximations, it is highly desirable that observers can independently dominate the effects of unmodelled dynamics. Based on adaptive nonlinear approximation, this paper presents a robust H∞ gain neuro-adaptive observer (R H∞GNAO) design methodology for a large class of uncertain nonlinear systems in the presence of time-varying unknown parameters with bounded external disturbances on the state vector and on the output of the original system. The proposed R H∞GNAO incorporates radial basis function neural networks (RBFNNs) to approximate the unknown nonlinearities in the uncertain system. The weight dynamics of every RBFNN are adjusted online by using an adaptive projection algorithm. The asymptotic convergence of the state and parameter estimation errors is achieved by using Lyapunov cogitation under a well-defined persistent excitation condition, and without recourse to the strictly positive real condition. The repercussions of unknown disturbances are reduced by integrating an H∞ gain performance criterion into the proposed estimation approach. The condition imposed by this proposed observer approach, such that all estimated signals are uniformly ultimately bounded, is expressed in the form of the linear matrix inequality problem and warrants the demanded performances. To evaluate the performance of the proposed observer, various simulations are presented.
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Angelopoulos, Spyros, Christoph Dürr, and Shendan Jin. "Online maximum matching with recourse." Journal of Combinatorial Optimization 40, no. 4 (2020): 974–1007. http://dx.doi.org/10.1007/s10878-020-00641-w.

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Avitabile, T., C. Mathieu, and L. Parkinson. "Online constrained optimization with recourse." Information Processing Letters 113, no. 3 (2013): 81–86. http://dx.doi.org/10.1016/j.ipl.2012.09.011.

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Wang, Jinde. "Approximate nonlinear programming algorithms for solving stochastic programs with recourse." Annals of Operations Research 31, no. 1 (1991): 371–84. http://dx.doi.org/10.1007/bf02204858.

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Kulkarni, Ankur A., and Uday V. Shanbhag. "Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms." Computational Optimization and Applications 51, no. 1 (2010): 77–123. http://dx.doi.org/10.1007/s10589-010-9316-8.

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Megow, Nicole, Martin Skutella, José Verschae, and Andreas Wiese. "The Power of Recourse for Online MST and TSP." SIAM Journal on Computing 45, no. 3 (2016): 859–80. http://dx.doi.org/10.1137/130917703.

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Smale, Steve, and Yuan Yao. "Online Learning Algorithms." Foundations of Computational Mathematics 6, no. 2 (2005): 145–70. http://dx.doi.org/10.1007/s10208-004-0160-z.

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BARBAKH, WESAM, and COLIN FYFE. "ONLINE CLUSTERING ALGORITHMS." International Journal of Neural Systems 18, no. 03 (2008): 185–94. http://dx.doi.org/10.1142/s0129065708001518.

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We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.
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Wang, Paul Y., Sainyam Galhotra, Romila Pradhan, and Babak Salimi. "Demonstration of generating explanations for black-box algorithms using Lewis." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2787–90. http://dx.doi.org/10.14778/3476311.3476345.

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Explainable artificial intelligence (XAI) aims to reduce the opacity of AI-based decision-making systems, allowing humans to scrutinize and trust them. Unlike prior work that attributes the responsibility for an algorithm's decisions to its inputs as a purely associational concept, we propose a principled causality-based approach for explaining black-box decision-making systems. We present the demonstration of Lewis, a system that generates explanations for black-box algorithms at the global, contextual, and local levels, and provides actionable recourse for individuals negatively affected by an algorithm's decision. Lewis makes no assumptions about the internals of the algorithm except for the availability of its input-output data. The explanations generated by Lewis are based on probabilistic contrastive counterfactuals, a concept that can be traced back to philosophical, cognitive, and social foundations of theories on how humans generate and select explanations. We describe the system layout of Lewis wherein an end-user specifies the underlying causal model and Lewis generates explanations for particular use-cases, compares them with explanations generated by state-of-the-art approaches in XAI, and provides actionable recourse when applicable. Lewis has been developed as open-source software; the code and the demonstration video are available at lewis-system.github.io.
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Dissertations / Theses on the topic "Online algorithms with recourse"

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Lowe, Wing Wah. "An exploration of stochastic decomposition algorithms for stochastic linear programs with recourse." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186667.

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Stochastic linear programs are linear programs in which some of the problem data are random variables. The particular kind of programs that we study belong to the recourse model. Under this model, some decisions are postponed until better information becomes available (e.g., an outcome of a random variable is realized), while other decisions must be made 'here and now.' For example, in a telecommunication network planning problem, decisions regarding the addition of network capacity have to be made before knowing customer demand (i.e., 'here and now'). Once the demand is realized, efficient usage of the network can then be determined. This work explores algorithms for the solution of such programs: stochastic linear programs with recourse. The algorithms investigated can be described as decomposition based cutting plane methods in which the cuts are estimated from random samples. Moreover, the algorithms all use the incremental sampling plan inherent to the Stochastic Decomposition (SD) algorithm developed by Higle and Sen in 1991. Our study includes both two stage and multistage programs. For the solution of two stage programs, we present the Conditional Stochastic Decomposition (CSD) algorithm, a multicut version of the SD algorithm. CSD is most suitable for situations in which data are difficult to obtain and may be computationally intense. Because of this potential intensity, we explore algorithms which require less computational effort than CSD. These algorithms combine features of both CSD and SD and are referred to as hybrid algorithms. Following our exploration of these algorithms for two stage problems, we next explore an extension of the SD algorithm that can be used for multistage problems with stagewise independent random variables. For the sake of notational brevity, our technical development is centered around the three stage case, although the extension to multistage problems is straightforward. Under mild conditions, convergence results similar to those found in the two stage algorithms hold. Multistage stochastic decomposition is currently a largely uncharted area. Our research represents the first major effort in this direction.
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Li, Le. "Online stochastic algorithms." Thesis, Angers, 2018. http://www.theses.fr/2018ANGE0031.

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Cette thèse travaille principalement sur trois sujets. Le premier concentre sur le clustering en ligne dans lequel nous présentons un nouvel algorithme stochastique adaptatif pour regrouper des ensembles de données en ligne. Cet algorithme repose sur l'approche quasi-bayésienne, avec une estimation dynamique (i.e., dépendant du temps) du nombre de clusters. Nous prouvons que cet algorithme atteint une borne de regret de l'ordre et que cette borne est asymptotiquement minimax sous la contrainte sur le nombre de clusters. Nous proposons aussi une implémentation par RJMCMC. Le deuxième sujet est lié à l'apprentissage séquentiel des courbes principales qui cherche à résumer une séquence des données par une courbe continue. Pour ce faire, nous présentons une procédure basée sur une approche maximum a posteriori pour le quasi-posteriori de Gibbs. Nous montrons que la borne de regret de cet algorithme et celui de sa version adaptative est sous-linéaire en l'horizon temporel T. En outre, nous proposons une implémentation par un algorithme glouton local qui intègre des éléments de sleeping experts et de bandit à plusieurs bras. Le troisième concerne les travaux qui visent à accomplir des tâches pratiques au sein d'iAdvize, l'entreprise qui soutient cette thèse. Il inclut l'analyse des sentiments pour les messages textuels et l'implémentation de chatbot dans lesquels la première est réalisé par les méthodes classiques dans la fouille de textes et les statistiques et la seconde repose sur le traitement du langage naturel et les réseaux de neurones artificiels<br>This thesis works mainly on three subjects. The first one is online clustering in which we introduce a new and adaptive stochastic algorithm to cluster online dataset. It relies on a quasi-Bayesian approach, with a dynamic (i.e., time-dependent) estimation of the (unknown and changing) number of clusters. We prove that this algorithm has a regret bound of the order of and is asymptotically minimax under the constraint on the number of clusters. A RJMCMC-flavored implementation is also proposed. The second subject is related to the sequential learning of principal curves which seeks to represent a sequence of data by a continuous polygonal curve. To this aim, we introduce a procedure based on the MAP of Gibbs-posterior that can give polygonal lines whose number of segments can be chosen automatically. We also show that our procedure is supported by regret bounds with sublinear remainder terms. In addition, a greedy local search implementation that incorporates both sleeping experts and multi-armed bandit ingredients is presented. The third one concerns about the work which aims to fulfilling practical tasks within iAdvize, the company which supports this thesis. It includes sentiment analysis for textual messages by using methods in both text mining and statistics, and implementation of chatbot based on nature language processing and neural networks
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Shi, Tian. "Novel Algorithms for Understanding Online Reviews." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104998.

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This dissertation focuses on the review understanding problem, which has gained attention from both industry and academia, and has found applications in many downstream tasks, such as recommendation, information retrieval and review summarization. In this dissertation, we aim to develop machine learning and natural language processing tools to understand and learn structured knowledge from unstructured reviews, which can be investigated in three research directions, including understanding review corpora, understanding review documents, and understanding review segments. For the corpus-level review understanding, we have focused on discovering knowledge from corpora that consist of short texts. Since they have limited contextual information, automatically learning topics from them remains a challenging problem. We propose a semantics-assisted non-negative matrix factorization model to deal with this problem. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of a corpus. We conduct extensive sets of experiments on several short text corpora to demonstrate the proposed model can discover meaningful and coherent topics. For document-level review understanding, we have focused on building interpretable and reliable models for the document-level multi-aspect sentiment analysis (DMSA) task, which can help us to not only recover missing aspect-level ratings and analyze sentiment of customers, but also detect aspect and opinion terms from reviews. We conduct three studies in this research direction. In the first study, we collect a new DMSA dataset in the healthcare domain and systematically investigate reviews in this dataset, including a comprehensive statistical analysis and topic modeling to discover aspects. We also propose a multi-task learning framework with self-attention networks to predict sentiment and ratings for given aspects. In the second study, we propose corpus-level and concept-based explanation methods to interpret attention-based deep learning models for text classification, including sentiment classification. The proposed corpus-level explanation approach aims to capture causal relationships between keywords and model predictions via learning importance of keywords for predicted labels across a training corpus based on attention weights. We also propose a concept-based explanation method that can automatically learn higher level concepts and their importance to model predictions. We apply these methods to the classification task and show that they are powerful in extracting semantically meaningful keywords and concepts, and explaining model predictions. In the third study, we propose an interpretable and uncertainty aware multi-task learning framework for DMSA, which can achieve competitive performance while also being able to interpret the predictions made. Based on the corpus-level explanation method, we propose an attention-driven keywords ranking method, which can automatically discover aspect terms and aspect-level opinion terms from a review corpus using the attention weights. In addition, we propose a lecture-audience strategy to estimate model uncertainty in the context of multi-task learning. For the segment-level review understanding, we have focused on the unsupervised aspect detection task, which aims to automatically extract interpretable aspects and identify aspect-specific segments from online reviews. The existing deep learning-based topic models suffer from several problems such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest. To deal with these problems, we propose a self-supervised contrastive learning framework in order to learn better representations for aspects and review segments. We also introduce a high-resolution selective mapping method to efficiently assign aspects discovered by the model to the aspects of interest. In addition, we propose using a knowledge distillation technique to further improve the aspect detection performance.<br>Doctor of Philosophy<br>Nowadays, online reviews are playing an important role in our daily lives. They are also critical to the success of many e-commerce and local businesses because they can help people build trust in brands and businesses, provide insights into products and services, and improve consumers' confidence. As a large number of reviews accumulate every day, a central research problem is to build an artificial intelligence system that can understand and interact with these reviews, and further use them to offer customers better support and services. In order to tackle challenges in these applications, we first have to get an in-depth understanding of online reviews. In this dissertation, we focus on the review understanding problem and develop machine learning and natural language processing tools to understand reviews and learn structured knowledge from unstructured reviews. We have addressed the review understanding problem in three directions, including understanding a collection of reviews, understanding a single review, and understanding a piece of a review segment. In the first direction, we proposed a short-text topic modeling method to extract topics from review corpora that consist of primary complaints of consumers. In the second direction, we focused on building sentiment analysis models to predict the opinions of consumers from their reviews. Our deep learning models can provide good prediction accuracy as well as a human-understandable explanation for the prediction. In the third direction, we develop an aspect detection method to automatically extract sentences that mention certain features consumers are interested in, from reviews, which can help customers efficiently navigate through reviews and help businesses identify the advantages and disadvantages of their products.
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Trippen, Gerhard Wolfgang. "Online exploration and search in graphs /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20TRIPPE.

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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.<br>published_or_final_version<br>Computer Science<br>Doctoral<br>Doctor of Philosophy
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ALBUQUERQUE, LUIZ FERNANDO FERNANDES DE. "ONLINE ALGORITHMS ANALYSIS FOR SPONSORED LINKS SELECTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16088@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>Links patrocinados são aqueles que aparecem em destaque nos resultados de pesquisas em máquinas de busca na Internet e são grande fonte de receita para seus provedores. Para os anunciantes, que fazem ofertas por palavras-chave para aparecerem em destaque nas consultas dos usuários, são uma oportunidade de divulgação da marca, conquista e manutenção de clientes. Um dos desafios das máquinas de busca neste modelo de negócio é selecionar os anunciantes que serão exibidos a cada consulta de modo a maximizar sua receita em determinado período. Este é um problema tipicamente online, onde a cada consulta é tomada uma decisão sem o conhecimento prévio das próximas consultas. Após uma decisão ser tomada, esta não pode mais ser alterada. Nesta dissertação avaliamos experimentalmente algoritmos propostos na literatura para solução deste problema, comparando-os à solução ótima offline, em simulações com dados sintéticos. Supondo que o conjunto das consultas diárias obedeça a uma determinada distribuição, propomos dois algoritmos baseados em informações estocásticas que são avaliados nos mesmos cenários que os outros algoritmos.<br>Sponsored links are those that appear highlighted at Internet search engine results. They are responsible for a large amount of their providers’ revenue. To advertisers, that place bids for keywords in large auctions at Internet, these links are the opportunity of brand exposing and achieving more clients. To search engine companies, one of the main challenges in this business model is selecting which advertisers should be allocated to each new query to maximize their total revenue in the end of the day. This is a typical online problem, where for each query is taken a decision without previous knowledge of future queries. Once the decision is taken, it can not be modified anymore. In this work, using synthetically generated data, we do experimental evaluation of three algorithms proposed in the literature for this problem and compare their results with the optimal offline solution. Considering that daily query set obeys some well known distribution, we propose two algorithms based on stochastic information, those are evaluated in the same scenarios of the others.
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Pasteris, S. U. "Efficient algorithms for online learning over graphs." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1516210/.

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In this thesis we consider the problem of online learning with labelled graphs, in particular designing algorithms that can perform this problem quickly and with low memory requirements. We consider the tasks of Classification (in which we are asked to predict the labels of vertices) and Similarity Prediction (in which we are asked to predict whether two given vertices have the same label). The first half of the thesis considers non- probabilistic online learning, where there is no probability distribution on the labelling and we bound the number of mistakes of an algorithm by a function of the labelling's complexity (i.e. its "naturalness"), often the cut- size. The second half of the thesis considers probabilistic machine learning in which we have a known probability distribution on the labelling. Before considering probabilistic online learning we first analyse the junction tree algorithm, on which we base our online algorithms, and design a new ver- sion of it, superior to the otherwise current state of the art. Explicitly, the novel contributions of this thesis are as follows: • A new algorithm for online prediction of the labelling of a graph which has better performance than previous algorithms on certain graph and labelling families. • Two algorithms for online similarity prediction on a graph (a novel problem solved in this thesis). One performs very well whilst the other not so well but which runs exponentially faster. • A new (better than before, in terms of time and space complexity) state of the art junction tree algorithm, as well as an application of it to the problem of online learning in an Ising model. • An algorithm that, in linear time, finds the optimal junction tree for online inference in tree-structured Ising models, the resulting online junction tree algorithm being far superior to the previous state of the art. All claims in this thesis are supported by mathematical proofs.
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Bonifaci, Vincenzo. "Models and algorithms for online server routing." Doctoral thesis, La Sapienza, 2007. http://hdl.handle.net/11573/917056.

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Harrington, Edward Francis. "Aspects of online learning /." View thesis entry in Australian Digital Theses Program, 2004. http://thesis.anu.edu.au/public/adt-ANU20060328.160810/index.html.

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Kamphans, Thomas. "Models and algorithms for online exploration and search." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=980408121.

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Books on the topic "Online algorithms with recourse"

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Fiat, Amos, and Gerhard J. Woeginger, eds. Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029561.

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Kaklamanis, Christos, and Asaf Levin, eds. Approximation and Online Algorithms. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2.

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Koenemann, Jochen, and Britta Peis, eds. Approximation and Online Algorithms. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92702-8.

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Chalermsook, Parinya, and Bundit Laekhanukit, eds. Approximation and Online Algorithms. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18367-6.

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Bampis, Evripidis, and Ola Svensson, eds. Approximation and Online Algorithms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18263-6.

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Sanità, Laura, and Martin Skutella, eds. Approximation and Online Algorithms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28684-6.

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Erlebach, Thomas, and Giuseppe Persiano, eds. Approximation and Online Algorithms. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38016-7.

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Solis-Oba, Roberto, and Giuseppe Persiano, eds. Approximation and Online Algorithms. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29116-6.

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Jansen, Klaus, and Monaldo Mastrolilli, eds. Approximation and Online Algorithms. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51741-4.

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Solis-Oba, Roberto, and Rudolf Fleischer, eds. Approximation and Online Algorithms. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89441-6.

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Book chapters on the topic "Online algorithms with recourse"

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Liu, Alison Hsiang-Hsuan, and Jonathan Toole-Charignon. "The Power of Amortized Recourse for Online Graph Problems." In Approximation and Online Algorithms. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18367-6_7.

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Gupta, Anupam, Vijaykrishna Gurunathan, Ravishankar Krishnaswamy, Amit Kumar, and Sahil Singla. "Online Discrepancy with Recourse for Vectors and Graphs." In Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). Society for Industrial and Applied Mathematics, 2022. http://dx.doi.org/10.1137/1.9781611977073.57.

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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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Albers, Susanne, and Jeffery Westbrook. "Self-organizing data structures." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029563.

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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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Chrobak, Marek, and Lawrence L. Larmore. "Metrical task systems, the server problem and the work function algorithm." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029565.

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Bartal, Yair. "Distributed paging." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029566.

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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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Csirik, János, and Gerhard J. Woeginger. "On-line packing and covering problems." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029568.

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Azar, Yossi. "On-line load balancing." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029569.

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Conference papers on the topic "Online algorithms with recourse"

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Fonseca, João, Andrew Bell, Carlo Abrate, Francesco Bonchi, and Julia Stoyanovich. "Setting the Right Expectations: Algorithmic Recourse Over Time." In EAAMO '23: Equity and Access in Algorithms, Mechanisms, and Optimization. ACM, 2023. http://dx.doi.org/10.1145/3617694.3623251.

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Krishnaswamy, Ravishankar, Shi Li, and Varun Suriyanarayana. "Online Unrelated-Machine Load Balancing and Generalized Flow with Recourse." In STOC '23: 55th Annual ACM Symposium on Theory of Computing. ACM, 2023. http://dx.doi.org/10.1145/3564246.3585222.

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Abé, M., and T. Igusa. "New Control Algorithms for Semi-Active Dynamic Vibration Absorbers." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0619.

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Abstract A semi-active dynamic vibration absorber is proposed for controlling the free-vibration impulse response of structures. It is assumed that (i) the initial displacement for the absorber spring can be set to non-zero values and (ii) the viscous damping coefficient for the absorber damping can be adjusted. The theory is first developed for a single-degree-of-freedom structure, and is then generalized to continuous structures. The extensive use of closed-form analytical results provides useful insight into the complex interaction between the structure and absorber. This makes it possible to solve the design problem without recourse to numerical optimization. The semi-active vibration absorber is found to be far more effective than conventional passive devices.
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Meng, De, Maryam Fazel, and Mehran Mesbahi. "Online algorithms for network formation." In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7798259.

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Bern, M., D. H. Greene, A. Raghunathan, and M. Sudan. "Online algorithms for locating checkpoints." In the twenty-second annual ACM symposium. ACM Press, 1990. http://dx.doi.org/10.1145/100216.100264.

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Meyerson, Adam. "Online algorithms for network design." In the sixteenth annual ACM symposium. ACM Press, 2004. http://dx.doi.org/10.1145/1007912.1007958.

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Kuh, Anthony, Muhammad Sharif Uddin, and Phyllis Ng. "Online unsupervised kernel learning algorithms." In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282179.

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Ramanathan, Dinesh, and Rajesh Gupta. "System level online power management algorithms." In the conference. ACM Press, 2000. http://dx.doi.org/10.1145/343647.343867.

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Uddin, Muhammad Sharif, and Anthony Kuh. "Online Unsupervised Kernel Affine Projection Algorithms." In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2018. http://dx.doi.org/10.23919/apsipa.2018.8659616.

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Andro-Vasko, James, Wolfgang Bein, Dara Nyknahad, and Hiro Ito. "Evaluation of Online Power-Down Algorithms." In 2015 12th International Conference on Information Technology - New Generations (ITNG). IEEE, 2015. http://dx.doi.org/10.1109/itng.2015.82.

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Reports on the topic "Online algorithms with recourse"

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Ur, Shmuel. Analysis of Online Algorithms for Organ Allocation. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada249361.

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Labrindis, Alexandros, and Nick Roussopoulos. A Performance Evaluation of Online Warehouse Update Algorithms. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada441038.

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Mathew, Jijo K., Christopher M. Day, Howell Li, and Darcy M. Bullock. Curating Automatic Vehicle Location Data to Compare the Performance of Outlier Filtering Methods. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317435.

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Agencies use a variety of technologies and data providers to obtain travel time information. The best quality data can be obtained from second-by-second tracking of vehicles, but that data presents many challenges in terms of privacy, storage requirements and analysis. More frequently agencies collect or purchase segment travel time based upon some type of matching of vehicles between two spatially distributed points. Typical methods for that data collection involve license plate re-identification, Bluetooth, Wi-Fi, or some type of rolling DSRC identifier. One of the challenges in each of these sampling techniques is to employ filtering techniques to remove outliers associated with trip chaining, but not remove important features in the data associated with incidents or traffic congestion. This paper describes a curated data set that was developed from high-fidelity GPS trajectory data. The curated data contained 31,621 vehicle observations spanning 42 days; 2550 observations had travel times greater than 3 minutes more than normal. From this baseline data set, outliers were determined using GPS waypoints to determine if the vehicle left the route. Two performance measures were identified for evaluating three outlier-filtering algorithms by the proportion of true samples rejected and proportion of outliers correctly identified. The effectiveness of the three methods over 10-minute sampling windows was also evaluated. The curated data set has been archived in a digital repository and is available online for others to test outlier-filtering algorithms.
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Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7732.

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling &amp; Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by knowledge, creativity, and innovation. It requires new approaches and methods for solving complex problems, discovering new opportunities, and creating value in various domains of science, business,and society. Computational intelligence and data science are two key disciplines that can provide such approaches and methods by exploiting the power of data, algorithms, models, and systems to enable intelligent decision making, learning, adaptation, optimization, and discovery. The papers in this proceedings cover a wide range of topics related to computational intelligence and data science for the innovation economy. They include theoretical foundations, novel techniques, and innovative applications. The papers were selected and revised based on the feedback from the program committe members and reviewers who ensured their high quality. We would like to thank all the authors who submitted their papers to M3E2 2022. We also appreciate the keynote speakers who shared their insights and visions on the current trends and future directions of computational intelligence and data science for the innovation economy. We acknowledge the support of our sponsors, partners, and organizers who made this conference possible despite the challenging circumstances caused by the ongoing war in Ukraine. Finally, we thank all the participants who attended the conference online and contributed to its success.
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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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