A Randomized Algorithm for Online Scheduling with Interval Conflicts

2015 ◽  
pp. 91-103 ◽  
Author(s):  
Marcin Bienkowski ◽  
Artur Kraska ◽  
Paweł Schmidt
Keyword(s):  
Randomized Algorithm ◽  
Online Scheduling

Randomized algorithm for the problem of two-cluster sequence splitting

2018 ◽  
Vol 58 (12) ◽  
pp. 2169-2178
Author(s):  
A. Kelmanov ◽  
◽  
S. Khamidullin ◽  
V. Khandeev ◽  
◽  
...  
Keyword(s):  
Randomized Algorithm ◽  
Cluster Sequence

Approximation Algorithms for Submodular Data Summarization with a Knapsack Constraint

2021 ◽  
Vol 5 (1) ◽  
pp. 1-31
Author(s):  
Kai Han ◽  
Shuang Cui ◽  
Tianshuai Zhu ◽  
Enpei Zhang ◽  
Benwei Wu ◽  
...  
Keyword(s):  
Approximation Algorithms ◽  
Fundamental Problem ◽  
Randomized Algorithm ◽  
Deterministic Algorithm ◽  
Submodular Function ◽  
Approximation Ratio ◽  
Performance Bounds ◽  
Data Summarization ◽  
Submodular Optimization ◽  
Knapsack Constraint

Data summarization, i.e., selecting representative subsets of manageable size out of massive data, is often modeled as a submodular optimization problem. Although there exist extensive algorithms for submodular optimization, many of them incur large computational overheads and hence are not suitable for mining big data. In this work, we consider the fundamental problem of (non-monotone) submodular function maximization with a knapsack constraint, and propose simple yet effective and efficient algorithms for it. Specifically, we propose a deterministic algorithm with approximation ratio 6 and a randomized algorithm with approximation ratio 4, and show that both of them can be accelerated to achieve nearly linear running time at the cost of weakening the approximation ratio by an additive factor of ε. We then consider a more restrictive setting without full access to the whole dataset, and propose streaming algorithms with approximation ratios of 8+ε and 6+ε that make one pass and two passes over the data stream, respectively. As a by-product, we also propose a two-pass streaming algorithm with an approximation ratio of 2+ε when the considered submodular function is monotone. To the best of our knowledge, our algorithms achieve the best performance bounds compared to the state-of-the-art approximation algorithms with efficient implementation for the same problem. Finally, we evaluate our algorithms in two concrete submodular data summarization applications for revenue maximization in social networks and image summarization, and the empirical results show that our algorithms outperform the existing ones in terms of both effectiveness and efficiency.

Multi-agent reinforcement learning for online scheduling in smart factories

2021 ◽  
Vol 72 ◽  
pp. 102202
Author(s):  
Tong Zhou ◽  
Dunbing Tang ◽  
Haihua Zhu ◽  
Zequn Zhang
Keyword(s):  
Reinforcement Learning ◽  
Online Scheduling ◽  
Multi Agent ◽  
Smart Factories

scholarly journals A hybrid recommender system based-on link prediction for movie baskets analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammadsadegh Vahidi Farashah ◽  
Akbar Etebarian ◽  
Reza Azmi ◽  
Reza Ebrahimzadeh Dastjerdi
Keyword(s):  
Recommender System ◽  
Link Prediction ◽  
Mean Squared Error ◽  
Spatial Clustering ◽  
Randomized Algorithm ◽  
Similarity Criterion ◽  
Absolute Error ◽  
Similarity Criteria ◽  
Online Systems ◽  
Squared Error

AbstractOver the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

Enabling Online Scheduling for Multi-Microgrid Systems: An Event-Triggered Approach

2020 ◽  
pp. 1-1
Author(s):  
Xiaodong Yang ◽  
Youbing Zhang ◽  
Hangfei Wu ◽  
Jinyu Wen ◽  
Shijie Cheng
Keyword(s):  
Online Scheduling ◽  
Event Triggered

A biased-randomized algorithm for optimizing efficiency in parametric earthquake (Re) insurance solutions

2020 ◽  
Vol 123 ◽  
pp. 105033
Author(s):  
Christopher Bayliss ◽  
Roberto Guidotti ◽  
Alejandro Estrada-Moreno ◽  
Guillermo Franco ◽  
Angel A. Juan
Keyword(s):  
Randomized Algorithm

Online Scheduling of Dynamic Task Graphs with Communication and Contention for Multiprocessors

2012 ◽  
Vol 23 (1) ◽  
pp. 126-133 ◽  
Author(s):  
P. Choudhury ◽  
P. P. Chakrabarti ◽  
R. Kumar
Keyword(s):  
Online Scheduling ◽  
Dynamic Task ◽  
Task Graphs
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