Efficient Neighborhood Covering Reduction with Submodular Function Optimization

2017 ◽  
pp. 505-514
Author(s):  
Qiang Chen ◽  
Xiaodong Yue ◽  
Jie Zhou ◽  
Yufei Chen
Keyword(s):  
Submodular Function ◽  
Function Optimization

Submodular Function Optimization for Motion Clustering and Image Segmentation

2019 ◽  
Vol 30 (9) ◽  
pp. 2637-2649 ◽  
Author(s):  
Jianbing Shen ◽  
Xingping Dong ◽  
Jianteng Peng ◽  
Xiaogang Jin ◽  
Ling Shao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Submodular Function ◽  
Function Optimization ◽  
Motion Clustering

scholarly journals Team Composition in PES2018 Using Submodular Function Optimization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 76194-76202 ◽  
Author(s):  
Yifeng Zeng ◽  
Gaoyang Shen ◽  
Bilian Chen ◽  
Jing Tang
Keyword(s):  
Submodular Function ◽  
Function Optimization ◽  
Team Composition

Matroid and Submodular Function Optimization

2014 ◽  
pp. 659-719
Author(s):  
George Nemhauser ◽  
Laurence Wolsey
Keyword(s):  
Submodular Function ◽  
Function Optimization

Differentiation Coherence Algorithm for Steady Power Flow Control

2015 ◽  
Vol 9 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Yang Liu-Lin ◽  
Hang Nai-Shan
Keyword(s):  
Flow Control ◽  
Extreme Point ◽  
Power Flow ◽  
Reactive Power ◽  
Small Deviation ◽  
Function Optimization ◽  
Flow Mode ◽  
Power Flow Control ◽  
Active And Reactive Power ◽  
Variable Inequality

This paper researched steady power flow control with variable inequality constraints. Since the inverse function of power flow equation is hard to obtain, differentiation coherence algorithm was proposed for variable inequality which is tightly constrained. By this method, tightly constrained variable inequality for variables adjustment relationships was analyzed. The variable constrained sensitivity which reflects variable coherence was obtained to archive accurate extreme equation for function optimization. The hybrid power flow mode of node power with branch power was structured. It also structured the minimum variable model correction equation with convergence and robot being same as conventional power flow. In fundamental analysis, the effect of extreme point was verified by small deviation from constrained extreme equation, and the constrained sensitivity was made for active and reactive power. It pointed out possible deviation by using simplified non-constrained sensitivity to deal with the optimization problem of active and reactive power. The control solutions for power flow for optimal control have been discussed as well. The examples of power flow control and voltage management have shown that the algorithm is simple and concentrated and shows the effect of differential coherence method for extreme point analysis.

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.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

Sign in / Sign up

Export Citation Format

Share Document