scholarly journals Tight Approximation Ratio for Minimum Maximal Matching

2019 ◽  
pp. 181-193 ◽  
Author(s):  
Szymon Dudycz ◽  
Mateusz Lewandowski ◽  
Jan Marcinkowski
Keyword(s):  
Approximation Ratio ◽  
Maximal Matching ◽  
Minimum Maximal Matching

Minimum-maximal matching in series-parallel graphs

1988 ◽  
Vol 33 (1) ◽  
pp. 98-105 ◽  
Author(s):  
M.B. Richey ◽  
R.Gary Parker
Keyword(s):  
Maximal Matching ◽  
In Series ◽  
Minimum Maximal Matching

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.

scholarly journals Combinatorial approximation of maximum k-vertex cover in bipartite graphs within ratio 0.7

2018 ◽  
Vol 52 (1) ◽  
pp. 305-314 ◽  
Author(s):  
Vangelis Th. Paschos
Keyword(s):  
Greedy Algorithm ◽  
Vertex Cover ◽  
Bipartite Graphs ◽  
Approximation Ratio ◽  
Combinatorial Algorithm

We propose and analyze a simple purely combinatorial algorithm for max k-vertex cover in bipartite graphs, achieving approximation ratio 0.7. The only combinatorial algorithm currently known until now for this problem is the natural greedy algorithm, that achieves ratio (e − 1)/e = 0.632.

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