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Journal articles on the topic 'Online algorithms with recourse'

Author: Grafiati

Published: 25 May 2024

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1

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Unnikrishnan, Avinash, and Miguel Figliozzi. "Online Freight Network Assignment Model with Transportation Disruptions and Recourse." Transportation Research Record: Journal of the Transportation Research Board 2224, no. 1 (2011): 17–25. http://dx.doi.org/10.3141/2224-03.

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12

Smeulders, Bart, Valentin Bartier, Yves Crama, and Frits C. R. Spieksma. "Recourse in Kidney Exchange Programs." INFORMS Journal on Computing 34, no. 2 (2022): 1191–206. http://dx.doi.org/10.1287/ijoc.2021.1099.

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We introduce the problem of selecting patient-donor pairs in a kidney exchange program to undergo a crossmatch test, and we model this selection problem as a two-stage stochastic integer programming problem. The optimal solutions of this new formulation yield a larger expected number of realized transplants than previous approaches based on internal recourse or subset recourse. We settle the computational complexity of the selection problem by showing that it remains NP-hard even for maximum cycle length equal to two. Furthermore, we investigate to what extent different algorithmic approaches, including one based on Benders decomposition, are able to solve instances of the model. We empirically investigate the computational efficiency of this approach by solving randomly generated instances and study the corresponding running times as a function of maximum cycle length, and of the presence of nondirected donors. Summary of Contribution: This paper deals with an important and very complex issue linked to the optimization of transplant matchings in kidney exchange programs, namely, the inherent uncertainty in the assessment of compatibility between donors and recipients of transplants. Although this issue has previously received some attention in the optimization literature, most attempts to date have focused on applying recourse to solutions selected within restricted spaces. The present paper explicitly formulates the maximization of the expected number of transplants as a two-stage stochastic integer programming problem. The formulation turns out to be computationally difficulty, both from a theoretical and from a numerical perspective. Different algorithmic approaches are proposed and tested experimentally for its solution. The quality of the kidney exchanges produced by these algorithms compares favorably with that of earlier models.

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13

Tometzki, Thomas, and Sebastian Engell. "Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical Investigation." Evolutionary Computation 17, no. 4 (2009): 511–26. http://dx.doi.org/10.1162/evco.2009.17.4.17404.

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In this contribution, we consider decision problems on a moving horizon with significant uncertainties in parameters. The information and decision structure on moving horizons enables recourse actions which correct the here-and-now decisions whenever the horizon is moved a step forward. This situation is reflected by a mixed-integer recourse model with a finite number of uncertainty scenarios in the form of a two-stage stochastic integer program. A stage decomposition-based hybrid evolutionary algorithm for two-stage stochastic integer programs is proposed that employs an evolutionary algorithm to determine the here-and-now decisions and a standard mathematical programming method to optimize the recourse decisions. An empirical investigation of the scale-up behavior of the algorithms with respect to the number of scenarios exhibits that the new hybrid algorithm generates good feasible solutions more quickly than a state of the art exact algorithm for problem instances with a high number of scenarios.

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14

Ying, Y., and D. X. Zhou. "Online Regularized Classification Algorithms." IEEE Transactions on Information Theory 52, no. 11 (2006): 4775–88. http://dx.doi.org/10.1109/tit.2006.883632.

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15

Boyar, Joan, Lene M. Favrholdt, Christian Kudahl, Kim S. Larsen, and Jesper W. Mikkelsen. "Online Algorithms with Advice." ACM Computing Surveys 50, no. 2 (2017): 1–34. http://dx.doi.org/10.1145/3056461.

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16

Albers, Susanne. "Online algorithms: a survey." Mathematical Programming 97, no. 1 (2003): 3–26. http://dx.doi.org/10.1007/s10107-003-0436-0.

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Ying, Yiming, and Ding-Xuan Zhou. "Online Pairwise Learning Algorithms." Neural Computation 28, no. 4 (2016): 743–77. http://dx.doi.org/10.1162/neco_a_00817.

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Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.

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18

Gilbert, Andrew Simon. "Algorithmic culture and the colonization of life-worlds." Thesis Eleven 146, no. 1 (2018): 87–96. http://dx.doi.org/10.1177/0725513618776699.

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This article explores some of the concerns which are being raised about algorithms with recourse to Habermas’s theory of communicative action. The intention is not to undertake an empirical examination of ‘algorithms’ or their consequences but to connect critical theory to some contemporary concerns regarding digital cultures. Habermas’s ‘colonization of life-worlds’ thesis gives theoretical expression to two different trends which underlie many current criticisms of the insidious influence of digital algorithms: the privatization of communication, and the particularization of knowledge and experience. Habermas’s social theory therefore offers a useful framework for exploring some of the normative and political problems that are attributed to ‘algorithmic culture’ and ‘big data’.

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19

Le Thi, Hoai An, and Vinh Thanh Ho. "Online Learning Based on Online DCA and Application to Online Classification." Neural Computation 32, no. 4 (2020): 759–93. http://dx.doi.org/10.1162/neco_a_01266.

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We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark classification data sets show the efficiency of our proposed algorithms in comparison with the state-of-the-art online classification algorithms.

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20

Siddig, Murwan, and Yongjia Song. "Adaptive partition-based SDDP algorithms for multistage stochastic linear programming with fixed recourse." Computational Optimization and Applications 81, no. 1 (2021): 201–50. http://dx.doi.org/10.1007/s10589-021-00323-1.

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21

Sand, G., J. Till, T. Tometzki, M. Urselmann, S. Engell, and M. Emmerich. "Engineered versus standard evolutionary algorithms: A case study in batch scheduling with recourse." Computers & Chemical Engineering 32, no. 11 (2008): 2706–22. http://dx.doi.org/10.1016/j.compchemeng.2007.09.006.

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22

Pay, Babak Saleck, and Yongjia Song. "Partition-based decomposition algorithms for two-stage Stochastic integer programs with continuous recourse." Annals of Operations Research 284, no. 2 (2017): 583–604. http://dx.doi.org/10.1007/s10479-017-2689-7.

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23

Ross, S., J. Pineau, S. Paquet, and B. Chaib-draa. "Online Planning Algorithms for POMDPs." Journal of Artificial Intelligence Research 32 (July 29, 2008): 663–704. http://dx.doi.org/10.1613/jair.2567.

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Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their complexity. Here, we focus on online approaches that alleviate the computational complexity by computing good local policies at each decision step during the execution. Online algorithms generally consist of a lookahead search to find the best action to execute at each time step in an environment. Our objectives here are to survey the various existing online POMDP methods, analyze their properties and discuss their advantages and disadvantages; and to thoroughly evaluate these online approaches in different environments under various metrics (return, error bound reduction, lower bound improvement). Our experimental results indicate that state-of-the-art online heuristic search methods can handle large POMDP domains efficiently.

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24

Ashlagi, Itai, Brendan Lucier, and Moshe Tennenholtz. "Equilibria of Online Scheduling Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 67–73. http://dx.doi.org/10.1609/aaai.v27i1.8631.

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We describe a model for competitive online scheduling algorithms. Two servers, each with a single observable queue, compete for customers. Upon arrival, each customer strategically chooses the queue with minimal expected wait time. Each scheduler wishes to maximize its number of customers, and can strategically select which scheduling algorithm, such as First-Come-First-Served (FCFS), to use for its queue. This induces a game played by the servers and the customers. We consider a non-Bayesian setting, where servers and customers play to maximize worst-case payoffs. We show that there is a unique subgame perfect safety-level equilibrium and we describe the associated scheduling algorithm (which is not FCFS). The uniqueness result holds for both randomized and deterministic algorithms, with a different equilibrium algorithm in each case. When the goal of the servers is to minimize competitive ratio, we prove that it is an equilibrium for each server to apply FCFS: each server obtains the optimal competitive ratio of 2.

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25

Sun, Peng, and Teng Zhao. "Deploying Robots and Online Algorithms." Applied Mechanics and Materials 325-326 (June 2013): 1058–61. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1058.

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The improvement of 802.11 mesh networks is a robust quagmire. In this work, we show the construction of link-level acknowledgements. In order to answer this grand challenge, we use linear-time epistemologies to disconfirm that 802.11 mesh networks and architecture can collaborate to fix this riddle.

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26

Blum, Avrim, Tuomas Sandholm, and Martin Zinkevich. "Online algorithms for market clearing." Journal of the ACM 53, no. 5 (2006): 845–79. http://dx.doi.org/10.1145/1183907.1183913.

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27

Lagrée, Paul, Olivier Cappé, Bogdan Cautis, and Silviu Maniu. "Algorithms for Online Influencer Marketing." ACM Transactions on Knowledge Discovery from Data 13, no. 1 (2019): 1–30. http://dx.doi.org/10.1145/3274670.

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ZHANG, YONG, YUXIN WANG, FRANCIS Y. L. CHIN, and HING-FUNG TING. "COMPETITIVE ALGORITHMS FOR ONLINE PRICING." Discrete Mathematics, Algorithms and Applications 04, no. 02 (2012): 1250015. http://dx.doi.org/10.1142/s1793830912500152.

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Given a seller with m items, a sequence of users {u1, u2, …} come one by one, the seller must set the unit price and assign some items to each user on his/her arrival. Items can be sold fractionally. Each ui has his/her value function vi(⋅) such that vi(x) is the highest unit price ui is willing to pay for x items. The objective is to maximize the revenue by setting the price and number of items for each user. In this paper, we have the following contributions: if the highest value h among all vi(x) is known in advance, we first show the lower bound of the competitive ratio is ⌊ log h⌋/2, then give an online algorithm with competitive ratio 4⌊ log h⌋ + 6; if h is not known in advance, we give an online algorithm with competitive ratio 2⋅h log -1/2 h + 8⋅h3 log -1/2 h.

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29

Devanur, Nikhil R. "Online algorithms with stochastic input." ACM SIGecom Exchanges 10, no. 2 (2011): 40–49. http://dx.doi.org/10.1145/1998549.1998558.

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30

Bienkowski, Marcin, Martin Böhm, Jaroslaw Byrka, et al. "Online Algorithms for Multilevel Aggregation." Operations Research 68, no. 1 (2020): 214–32. http://dx.doi.org/10.1287/opre.2019.1847.

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31

Gatto, Michael, and Peter Widmayer. "On robust online scheduling algorithms." Journal of Scheduling 14, no. 2 (2009): 141–56. http://dx.doi.org/10.1007/s10951-009-0115-y.

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32

Ying, Yiming. "Convergence analysis of online algorithms." Advances in Computational Mathematics 27, no. 3 (2006): 273–91. http://dx.doi.org/10.1007/s10444-005-9002-z.

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33

Ying, Yiming, and Massimiliano Pontil. "Online Gradient Descent Learning Algorithms." Foundations of Computational Mathematics 8, no. 5 (2007): 561–96. http://dx.doi.org/10.1007/s10208-006-0237-y.

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34

Kumar, Sandeep, and Deepak Garg. "Online Financial Algorithms: Competitive Analysis." International Journal of Computer Applications 40, no. 7 (2012): 8–14. http://dx.doi.org/10.5120/4974-7228.

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35

Liu, Yuxin, Yi Mei, Mengjie Zhang, and Zili Zhang. "A Predictive-Reactive Approach with Genetic Programming and Cooperative Coevolution for the Uncertain Capacitated Arc Routing Problem." Evolutionary Computation 28, no. 2 (2020): 289–316. http://dx.doi.org/10.1162/evco_a_00256.

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The uncertain capacitated arc routing problem is of great significance for its wide applications in the real world. In the uncertain capacitated arc routing problem, variables such as task demands and travel costs are realised in real time. This may cause the predefined solution to become ineffective and/or infeasible. There are two main challenges in solving this problem. One is to obtain a high-quality and robust baseline task sequence, and the other is to design an effective recourse policy to adjust the baseline task sequence when it becomes infeasible and/or ineffective during the execution. Existing studies typically only tackle one challenge (the other being addressed using a naive strategy). No existing work optimises the baseline task sequence and recourse policy simultaneously. To fill this gap, we propose a novel proactive-reactive approach, which represents a solution as a baseline task sequence and a recourse policy. The two components are optimised under a cooperative coevolution framework, in which the baseline task sequence is evolved by an estimation of distribution algorithm, and the recourse policy is evolved by genetic programming. The experimental results show that the proposed algorithm, called Solution-Policy Coevolver, significantly outperforms the state-of-the-art algorithms to the uncertain capacitated arc routing problem for the ugdb and uval benchmark instances. Through further analysis, we discovered that route failure is not always detrimental. Instead, in certain cases (e.g., when the vehicle is on the way back to the depot) allowing route failure can lead to better solutions.

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36

Butler, Owen, and Darren Musgrove. "72 Elemental Screening of Welding Fumes by Ed-Xrf Using a Standardless Calibration Algorithm." Annals of Work Exposures and Health 67, Supplement_1 (2023): i22—i23. http://dx.doi.org/10.1093/annweh/wxac087.060.

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Abstract X-ray fluorescence (XRF) is an ideal analytical technique for rapid elemental analysis in supporting forensic analysis, for exposure assessments and for product compositional analysis. Standardless calibration algorithms are widely used in modern XRF systems so providing, in many cases, near quantitative analysis, without recourse to needing matrix-matched standards. However if available, matrix standards can be used to further improve the performance of such algorithms and so provide better quantitative data. In this presentation, XRF analysis of bulk welding fume samples was performed, in supporting fume compositional studies required for product data sheets, and aided by the analysis of reference welding fume standards previously characterised by ICP-AES.

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Dixit, Rishabh, Amrit Singh Bedi, Ruchi Tripathi, and Ketan Rajawat. "Online Learning With Inexact Proximal Online Gradient Descent Algorithms." IEEE Transactions on Signal Processing 67, no. 5 (2019): 1338–52. http://dx.doi.org/10.1109/tsp.2018.2890368.

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Goyal, Shashank, and Diwakar Gupta. "The Online Reservation Problem." Algorithms 13, no. 10 (2020): 241. http://dx.doi.org/10.3390/a13100241.

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Many sharing-economy platforms operate as follows. Owners list the availability of resources, prices, and contract-length limits. Customers propose contract start times and lengths. The owners decide immediately whether to accept or decline each proposal, even if the contract is for a future date. Accepted proposals generate revenue. Declined proposals are lost. At any decision epoch, the owner has no information regarding future proposals. The owner seeks easy-to-implement algorithms that achieve the best competitive ratio (CR). We first derive a lower bound on the CR of any algorithm. We then analyze CRs of all intuitive “greedy” algorithms. We propose two new algorithms that have significantly better CRs than that of any greedy algorithm for certain parameter-value ranges. The key idea behind these algorithms is that owners may reserve some amount of capacity for late-arriving higher-value proposals in an attempt to improve revenue. Our contribution lies in operationalizing this idea with the help of algorithms that utilize thresholds. Moreover, we show that if non-optimal thresholds are chosen, then those may lead to poor CRs. We provide a rigorous method by which an owner can decide the best approach in their context by analyzing the CRs of greedy algorithms and those proposed by us.

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Huang, Sabrina Angela, Jeffrey Hancock, and Stephanie Tom Tong. "Folk Theories of Online Dating: Exploring People’s Beliefs About the Online Dating Process and Online Dating Algorithms." Social Media + Society 8, no. 2 (2022): 205630512210895. http://dx.doi.org/10.1177/20563051221089561.

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How do online daters come to understand and make sense of their online dating experiences and the algorithms that underlie online dating platforms? Across two mixed-method studies, we take a metaphoric approach to identify and explore people’s folk theories about traditional dating, online dating, and online dating algorithms. In Study 1, we take a quantitative approach and use an innovative wiki-survey procedure to identify individuals’ folk theories of online dating and their associated themes through content analyses. In Study 2, we take a qualitative approach, exploring participants’ folk theories through in-depth interviews, extended case method, and grounded theory. Our studies uncovered two folk theories unique to traditional dating ( movies, nurturing), one folk theory unique to online dating ( game), three folk theories related to online dating algorithms ( filter, personalized advertisements, bracket), and two folk theories that were found to overlap between traditional and online dating ( shopping, chance and randomness). Our findings provide novel insights into how daters make sense of traditional and online relationship development processes as well as the algorithms that underlie online dating platforms.

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40

Baranova, Tatiana, Aleksandra Kobicheva, Elena Tokareva, and Charles Bryant. "Online examination session: teachers’ and students’ satisfaction level." E3S Web of Conferences 284 (2021): 09003. http://dx.doi.org/10.1051/e3sconf/202128409003.

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This paper is aimed at defining the satisfaction level of students and teachers with online examination session. To implement the study, we conducted online surveys for 2nd year bachelor students and 1st year master students (N=97) and online interviews with teachers (N=12). The analysis of received data indicated that students felt more satisfied with online examination session than teachers due to a number of reasons. Firstly, elder teachers faced technical problems on some steps of examination session. Secondly, teachers worried about the reliability of students’ testing results as they had access to any recourse or material and due to bad connection sometimes it was hard to hear or see students. Thus, some problems were revealed despite the general positive attitude to online examination session from students.

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Rosenkrantz, Daniel J., Giri K. Tayi, and S. S. Ravi. "Obtaining online approximation algorithms for facility dispersion from offline algorithms." Networks 47, no. 4 (2006): 206–17. http://dx.doi.org/10.1002/net.20109.

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42

Liu, Zhicheng, Ling Chen, Hong Chang, Donglei Du, and Xiaoyan Zhang. "Online algorithms for BP functions maximization." Theoretical Computer Science 858 (February 2021): 114–21. http://dx.doi.org/10.1016/j.tcs.2021.01.020.

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43

Höhne, Felix, Sören Schmitt, and Rob van Stee. "SIGACT News Online Algorithms Column 36." ACM SIGACT News 51, no. 4 (2020): 89–107. http://dx.doi.org/10.1145/3444815.3444830.

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In this column, we will discuss some papers in online algorithms that appeared in 2020. As usual, we make no claim at complete coverage here, and have instead made a selection. If we have unaccountably missed your favorite paper and you would like to write about it or about any other topic in online algorithms, please don't hesitate to contact us!

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Thiessen, Ernest, and Peter Holt. "Online Collaboration Algorithms for Small Claims." International Journal of Online Dispute Resolution 6, no. 2 (2019): 209–11. http://dx.doi.org/10.5553/ijodr/235250022019006002013.

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van Stee, Rob. "SIGACT News Online Algorithms Column 39." ACM SIGACT News 53, no. 2 (2022): 83. http://dx.doi.org/10.1145/3544979.3544992.

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For this issue, I am pleased to present an extensive survey by Debasis Dwibedy and Rakesh Mohanty on online makespan scheduling. This is a topic which continues to inspire new research and it is great that these authors have provided an updated survey.

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Yang, Tianbao, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, and Steven Hoi. "Online Kernel Selection: Algorithms and Evaluations." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1197–203. http://dx.doi.org/10.1609/aaai.v26i1.8298.

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Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational cost in computing the full kernel matrices and in training, especially when the number of kernels or the number of training examples is very large. In this paper, we tackle this problem by proposing an efficient online kernel selection algorithm. It incrementally learns a weight for each kernel classifier. The weight for each kernel classifier can help us to select a good kernel among a set of given kernels. The proposed approach is efficient in that (i) it is an online approach and therefore avoids computing all the full kernel matrices before training; (ii) it only updates a single kernel classifier each time by a sampling technique and therefore saves time on updating kernel classifiers with poor performance; (iii) it has a theoretically guaranteed performance compared to the best kernel predictor. Empirical studies on image classification tasks demonstrate the effectiveness of the proposed approach for selecting a good kernel among a set of kernels.

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van Stee, Rob. "SIGACT News Online Algorithms Column 37." ACM SIGACT News 52, no. 2 (2021): 71. http://dx.doi.org/10.1145/3471469.3471480.

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For this issue, Pavel Vesely has contributed a wonderful overview of the ideas that were used in his SODA paper on packet scheduling with Marek Chrobak, Lukasz Jez and Jiri Sgall. This is a problem for which a 2-competitive algorithm as well as a lower bound of ϕ ≈ 1:618 was known already twenty years ago, but which resisted resolution for a long time. It is great that this problem has nally been resolved and that Pavel was willing to explain more of the ideas behind it for this column. He also provides an overview of open problems in this area.

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Hohne, Felix, Soren Schmitt, and Rob van Stee. "SIGACT News Online Algorithms Column 38." ACM SIGACT News 52, no. 4 (2021): 80–96. http://dx.doi.org/10.1145/3510382.3510396.

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In this column, we will discuss some papers in online algorithms that appeared in 2021. As usual, we make no claim at complete coverage here, and have instead made a selection. If we have unaccountably missed your favorite paper and you would like to write about it or about any other topic in online algorithms, please don't hesitate to contact us!

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van Stee, Rob. "SIGACT News Online Algorithms Column 27." ACM SIGACT News 47, no. 1 (2016): 99–110. http://dx.doi.org/10.1145/2902945.2902960.

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Chrobak, Marek. "SIGACT news online algorithms column 4." ACM SIGACT News 35, no. 3 (2004): 58–66. http://dx.doi.org/10.1145/1027914.1027930.

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