A fast double greedy algorithm for non-monotone DR-submodular function maximization

Gu, Shuyang; Shi, Ganquan; Wu, Weili; Lu, Changhong

doi:10.1142/s179383092050007x

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…They proposed a double greedy algorithm, which has 1 2+ -approximation and O( n log 2 B). Subsequently, Gu et al (2020) [24] study the problem of maximizing the non-monotone DR-submodular function on the bounded integer lattice. They propose a fast double greedy algorithm that improves runtime.…”

Section: Related Workmentioning

confidence: 99%

“…Two notable approaches to this problem are greedy algorithms [16][17][18][19] and streaming algorithms [20][21][22]. Plenty of studies show that the greedy method is often used for this optimization problem because it outputs a better result than other methods due to its "greedy" operation [16,23,24]. Understandably, the greedy method always scans data many times to find the best.…”

Section: Introductionmentioning

confidence: 99%

“…These publications consider the problem under many different constraints and use greedy or streaming methods as the standard approach. Some prominent examples include the use of a fast double greedy algorithm to maximize the non-monotone DR submodular function [24], using a threshold greedy algorithm to maximize the monotone DR submodular constraint knapsack over an integer lattice [28], combining the threshold greedy algorithm with a partial element enumeration technique to maximize the monotone DR submodular knapsack constraint over an integer lattice [11], using a streaming method to maximize DR-submodular functions with d-knapsack constraints [29], using a one-pass streaming algorithm for DR-submodular maximization with a knapsack constraint over the integer lattice [30], and using streaming algorithms for maximizing a monotone DRsubmodular function with a cardinality constraint on the integer lattice [31].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Streaming Algorithms for Maximizing Monotone DR-Submodular Function on the Integer Lattice

Nguyen

Phạm

et al. 2022

Mathematics

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In recent years, the issue of maximizing submodular functions has attracted much interest from research communities. However, most submodular functions are specified in a set function. Meanwhile, recent advancements have been studied for maximizing a diminishing return submodular (DR-submodular) function on the integer lattice. Because plenty of publications show that the DR-submodular function has wide applications in optimization problems such as sensor placement impose problems, optimal budget allocation, social network, and especially machine learning. In this research, we propose two main streaming algorithms for the problem of maximizing a monotone DR-submodular function under cardinality constraints. Our two algorithms, which are called StrDRS1 and StrDRS2, have (1/2−ϵ), (1−1/e−ϵ) of approximation ratios and O(nϵlog(logBϵ)logk), O(nϵlogB), respectively. We conducted several experiments to investigate the performance of our algorithms based on the budget allocation problem over the bipartite influence model, an instance of the monotone submodular function maximization problem over the integer lattice. The experimental results indicate that our proposed algorithms not only provide solutions with a high value of the objective function, but also outperform the state-of-the-art algorithms in terms of both the number of queries and the running time.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Streaming Algorithms for Maximizing Monotone DR-Submodular Function on the Integer Lattice

Nguyen

Phạm

et al. 2022

Mathematics

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“…These algorithms show effective improvement and performance improvement for power scheduling problems with different time delays. The existing routing algorithms for IoT awareness are mainly planar, while the traditional hierarchical routing algorithms for IoT applications are mainly aimed at isomorphic sensor networks; that is, the initial energy of each node is the same [26]. The nodes in the data acquisition wireless sensor network are heterogeneous; that is, the energy is different.…”

Section: Routing Protocol For Energy Acquisition In Wireless Sensor Networkmentioning

confidence: 99%

Cross‐Modal Travel Route Recommendation Algorithm Based on Internet of Things Awareness

Zhang¹,

Tang

2021

Journal of Sensors

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In recent years, the Internet of Things has developed rapidly in people’s lives. This brand-new technology is flooding people’s lives and widely used in many fields, such as medical field, science and technology field, and industry and agriculture field. As a modern technology, the Internet of Things has many characteristics of low power consumption and multifunction, and it also has the characteristics of data-aware computing. This is the characteristic of this new product. In people’s daily life, the Internet of Things is also closely related to people’s daily life. In the tourism industry, the Internet of Things can make the best use of everything and give full play to its various advantages as much as possible. The Internet of Things can perceive cross-modal tourism routes. So here, this paper summarizes various algorithms recommended by the Internet of Things for this tourist route and works out the experimental data methods of these algorithms for cross-modal tourism route recommendation. The proposed algorithm is verified by data simulation, compared with related algorithms. We analyze and summarize the simulation results. At present, there is no comparative analysis of the performance of ant colony algorithm, genetic algorithm, and its optimization algorithm in tourism route recommendation. On the basis of crawling the tourism data in the Internet, this paper applies ant colony algorithm, genetic algorithm, max–min optimization ant colony algorithm, and hybrid ant colony algorithm based on greedy solution to tourism route recommendation and evaluates and compares the algorithms from three aspects: average evaluation score, optimal evaluation score, and algorithm time. Experimental results show that the max–min optimization ant colony algorithm and the hybrid ant colony algorithm based on greedy solution can be effectively applied to automated tourist route recommendation.

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Greedy Approximation and Submodular Optimization

Pardalos

et al. 2022

Introduction to Combinatorial Optimization

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A fast double greedy algorithm for non-monotone DR-submodular function maximization

Cited by 6 publications

References 12 publications

Efficient Streaming Algorithms for Maximizing Monotone DR-Submodular Function on the Integer Lattice

Efficient Streaming Algorithms for Maximizing Monotone DR-Submodular Function on the Integer Lattice

Cross‐Modal Travel Route Recommendation Algorithm Based on Internet of Things Awareness

Greedy Approximation and Submodular Optimization

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