Approximation Algorithms for the Minimum Convex Partition Problem

2006 ◽  
pp. 232-241 ◽  
Author(s):  
Christian Knauer ◽  
Andreas Spillner
Keyword(s):  
Approximation Algorithms ◽  
Partition Problem ◽  
Convex Partition

A Note on the Value in the Disjoint Convex Partition Problem

2018 ◽  
Vol 104 (1-2) ◽  
pp. 135-149 ◽  
Author(s):  
Xinshang You ◽  
Tong Chen
Keyword(s):  
Partition Problem ◽  
Convex Partition ◽  
Disjoint Convex

scholarly journals Some approximation algorithms for the clique partition problem in weighted interval graphs

2007 ◽  
Vol 381 (1-3) ◽  
pp. 124-133 ◽  
Author(s):  
Mingxia Chen ◽  
Jianbo Li ◽  
Jianping Li ◽  
Weidong Li ◽  
Lusheng Wang
Keyword(s):  
Approximation Algorithms ◽  
Interval Graphs ◽  
Partition Problem

scholarly journals One-Half Approximation Algorithms for the k-Partition Problem

1992 ◽  
Vol 40 (1-supplement-1) ◽  
pp. S170-S173 ◽  
Author(s):  
Thomas Feo ◽  
Olivier Goldschmidt ◽  
Mallek Khellaf
Keyword(s):  
Approximation Algorithms ◽  
Partition Problem

The d-Subtree Partition Problem

2010 ◽  
Vol 33 (4) ◽  
pp. 652-665
Author(s):  
Yan-Guang CAI ◽  
Yun ZHANG ◽  
Ji-Xin QIAN
Keyword(s):  
Partition Problem

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.

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