Academic literature on the topic 'Explorable uncertainty'
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Journal articles on the topic "Explorable uncertainty"
Focke, Jacob, Nicole Megow, and Julie Meißner. "Minimum Spanning Tree under Explorable Uncertainty in Theory and Experiments." ACM Journal of Experimental Algorithmics 25 (November 8, 2020): 1–20. http://dx.doi.org/10.1145/3422371.
Full textMansour, Yishay, Alex Slivkins, Vasilis Syrgkanis, and Zhiwei Steven Wu. "Bayesian Exploration: Incentivizing Exploration in Bayesian Games." Operations Research 70, no. 2 (March 2022): 1105–27. http://dx.doi.org/10.1287/opre.2021.2205.
Full textMathwieser, Corinna, and Eranda Çela. "Special cases of the minimum spanning tree problem under explorable edge and vertex uncertainty." Networks, January 11, 2024. http://dx.doi.org/10.1002/net.22204.
Full textErlebach, Thomas, Michael Hoffmann, and Murilo Santos de Lima. "Round-Competitive Algorithms for Uncertainty Problems with Parallel Queries." Algorithmica, September 15, 2022. http://dx.doi.org/10.1007/s00453-022-01035-6.
Full textAnselmi, Jonatha, and Josu Doncel. "Load Balancing with Job-Size Testing: Performance Improvement or Degradation?" ACM Transactions on Modeling and Performance Evaluation of Computing Systems, March 4, 2024. http://dx.doi.org/10.1145/3651154.
Full textDissertations / Theses on the topic "Explorable uncertainty"
Dogeas, Konstantinos. "Energy Minimization, Data Movement and Uncertainty : Models and Algorithms." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS070.pdf.
Full textHigh performance computers (HPCs) is the go-to solution for running computationally demanding applications. As the limit of energy consumption is already achieved, the need for more energy efficient algorithms is critical.Taking advantage of the core characteristics of an HPC, such as its network topology and the heterogeneity of the machines, could lead to better scheduling algorithms. In addition, designing more realistic models, that grasp the features of real-life applications, is a work in the same direction of achieving better performance. Allowing scheduling algorithms to decide either the amount of resources allocated to an application or the running speed of the resources can pave the path to new platform-aware implementations. In the first part of the thesis, we introduce a model which takes into account both the topology and the heterogeneity of a platform by introducing two kind of machines. We augment the scheduling problem with constraints whose purpose is to implicitly reduce data movement either during parallel execution or during the communication with the file system. We propose algorithms that can decide the number of resources allocated to an application taking into consideration the extra constraints.In the second part of the thesis, we deal with the uncertainty on part of the input and more specifically, the workload of an application, that is strictly related to the time needed for its completion. Most works in the literature consider this value known in advance. However, this is rarely the case in real-life systems.In our approach, the given workload is a worst case scenario for the execution of an application. We introduce application-specific tests that may decrease the workload of a task.Since the test (e.g. compression) takes some time, and since the amount of reduction (e.g. in size) is unknown before the completion of the test, the decision of running the test for a task or not has to be taken. We propose competitive algorithms for the problem of scheduling such tasks, in order to minimize the energy consumed in a set of speed-adjustable machines. In the third part of the thesis, we focus on a similar setting of uncertain input and we consider a model where the processing times are not known in advance. Here, we augment the input of the problem by introducing predicted values in place of the unknown processing times. We design algorithms that perform optimally when the predictions are accurate while remaining competitive to the best known ones otherwise
Book chapters on the topic "Explorable uncertainty"
Megow, Nicole, and Jens Schlöter. "Explorable Uncertainty Meets Decision-Making in Logistics." In Dynamics in Logistics, 35–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88662-2_2.
Full textErlebach, Thomas. "Computing and Scheduling with Explorable Uncertainty." In Sailing Routes in the World of Computation, 156–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94418-0_16.
Full textLiu, Alison Hsiang-Hsuan, Fu-Hong Liu, Prudence W. H. Wong, and Xiao-Ou Zhang. "The Power of Amortization on Scheduling with Explorable Uncertainty." In Approximation and Online Algorithms, 90–103. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49815-2_7.
Full textAlbers, Susanne, and Alexander Eckl. "Explorable Uncertainty in Scheduling with Non-uniform Testing Times." In Approximation and Online Algorithms, 127–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2_9.
Full textMegow, Nicole, and Jens Schlöter. "Set Selection Under Explorable Stochastic Uncertainty via Covering Techniques." In Integer Programming and Combinatorial Optimization, 319–33. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32726-1_23.
Full textConference papers on the topic "Explorable uncertainty"
Bampis, Evripidis, Konstantinos Dogeas, Alexander Kononov, Giorgio Lucarelli, and Fanny Pascual. "Speed Scaling with Explorable Uncertainty." In SPAA '21: 33rd ACM Symposium on Parallelism in Algorithms and Architectures. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3409964.3461812.
Full textErlebach, Thomas, Murilo de Lima, Nicole Megow, and Jens Schlöter. "Sorting and Hypergraph Orientation under Uncertainty with Predictions." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/619.
Full textMauricio, Cristóbal Alfredo, Sebastian Davila-Gálvez, and Óscar Carlos Vasquez. "When a test-taking strategy is better? An approach from the paradigm of scheduling under explorable uncertainty." In Ninth International Conference on Higher Education Advances. Valencia: Universitat Politècnica de València, 2023. http://dx.doi.org/10.4995/head23.2023.16371.
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