An online algorithm for the risk-aware restless bandit

2021 ◽  
Vol 290 (2) ◽  
pp. 622-639
Author(s):  
Jianyu Xu ◽  
Lujie Chen ◽  
Ou Tang
Keyword(s):  
Online Algorithm ◽  
Restless Bandit

scholarly journals Online Makespan Scheduling with Job Migration on Uniform Machines

Algorithmica ◽  
2021 ◽  
Author(s):  
Matthias Englert ◽  
David Mezlaf ◽  
Matthias Westermann
Keyword(s):  
Competitive Ratio ◽  
Online Algorithm ◽  
Parallel Machines ◽  
Scheduling Algorithm ◽  
Input Sequence ◽  
Job Migration ◽  
Average Completion Time ◽  
Uniform Machine ◽  
Uniform Machines ◽  
Completion Times

AbstractIn the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with sizes. A scheduling algorithm has to assign the jobs to m parallel machines. The objective is to minimize the makespan, which is the time it takes until all jobs are processed. In this paper, we consider online scheduling algorithms without preemption. However, we allow the online algorithm to change the assignment of up to k jobs at the end for some limited number k. For m identical machines, Albers and Hellwig (Algorithmica 79(2):598–623, 2017) give tight bounds on the competitive ratio in this model. The precise ratio depends on, and increases with, m. It lies between 4/3 and $$\approx 1.4659$$ ≈ 1.4659 . They show that $$k = O(m)$$ k = O ( m ) is sufficient to achieve this bound and no $$k = o(n)$$ k = o ( n ) can result in a better bound. We study m uniform machines, i.e., machines with different speeds, and show that this setting is strictly harder. For sufficiently large m, there is a $$\delta = \varTheta (1)$$ δ = Θ ( 1 ) such that, for m machines with only two different machine speeds, no online algorithm can achieve a competitive ratio of less than $$1.4659 + \delta $$ 1.4659 + δ with $$k = o(n)$$ k = o ( n ) . We present a new algorithm for the uniform machine setting. Depending on the speeds of the machines, our scheduling algorithm achieves a competitive ratio that lies between 4/3 and $$\approx 1.7992$$ ≈ 1.7992 with $$k = O(m)$$ k = O ( m ) . We also show that $$k = \varOmega (m)$$ k = Ω ( m ) is necessary to achieve a competitive ratio below 2. Our algorithm is based on maintaining a specific imbalance with respect to the completion times of the machines, complemented by a bicriteria approximation algorithm that minimizes the makespan and maximizes the average completion time for certain sets of machines.

Complete Online Algorithm for Air Data System Calibration

2019 ◽  
Vol 56 (2) ◽  
pp. 517-528 ◽  
Author(s):  
Juan D. Jurado ◽  
Clark C. McGehee
Keyword(s):  
Online Algorithm ◽  
Data System ◽  
System Calibration

An efficient distributed online algorithm to detect strong conjunctive predicates

2002 ◽  
Vol 28 (11) ◽  
pp. 1077-1084 ◽  
Author(s):  
Loon-Been Chen ◽  
I-Chen Wu
Keyword(s):  
Online Algorithm

scholarly journals An Offline and Online Algorithm for All Minimal k|U| Parameter Subsets of a Soft Set Based on Integer Partition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 192393-192407
Author(s):  
Shengling Geng ◽  
Banghe Han ◽  
Ruize Wu ◽  
Runqing Xu
Keyword(s):  
Online Algorithm ◽  
Soft Set ◽  
Integer Partition

scholarly journals End-to-End Transition-Based Online Dialogue Disentanglement

2020 ◽  
Author(s):  
Hui Liu ◽  
Zhan Shi ◽  
Jia-Chen Gu ◽  
Quan Liu ◽  
Si Wei ◽  
...  
Keyword(s):  
Large Scale ◽  
Online Algorithm ◽  
Research Field ◽  
Semantic Coherence ◽  
Large Scale Dataset ◽  
End To End ◽  
Sequential Information ◽  
Almost All ◽  
Classification And Clustering ◽  
Sequential Semantics

Dialogue disentanglement aims to separate intermingled messages into detached sessions. The existing research focuses on two-step architectures, in which a model first retrieves the relationships between two messages and then divides the message stream into separate clusters. Almost all existing work puts significant efforts on selecting features for message-pair classification and clustering, while ignoring the semantic coherence within each session. In this paper, we introduce the first end-to- end transition-based model for online dialogue disentanglement. Our model captures the sequential information of each session as the online algorithm proceeds on processing a dialogue. The coherence in a session is hence modeled when messages are sequentially added into their best-matching sessions. Meanwhile, the research field still lacks data for studying end-to-end dialogue disentanglement, so we construct a large-scale dataset by extracting coherent dialogues from online movie scripts. We evaluate our model on both the dataset we developed and the publicly available Ubuntu IRC dataset [Kummerfeld et al., 2019]. The results show that our model significantly outperforms the existing algorithms. Further experiments demonstrate that our model better captures the sequential semantics and obtains more coherent disentangled sessions.

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