Stochastic greedy algorithms for maximizing constrained submodular + supermodular functions

2021 ◽  
Author(s):  
Sai Ji ◽  
Dachuan Xu ◽  
Min Li ◽  
Yishui Wang ◽  
Dongmei Zhang
Keyword(s):  
Greedy Algorithms ◽  
Supermodular Functions

On Jackknifed Greedy Algorithms and Their Applications in NMR

2020 ◽  
Vol 84 (11) ◽  
pp. 1335-1340
Author(s):  
P. Kasprzak ◽  
K. Kazimierczuk ◽  
A. L. Shchukina
Keyword(s):  
Greedy Algorithms

Characterizing Mixability of Supermodular Functions

2014 ◽  
Author(s):  
Valeria Bignozzi ◽  
Giovanni Puccetti
Keyword(s):  
Supermodular Functions

scholarly journals Actuator Placement under Structural Controllability using Forward and Reverse Greedy Algorithms

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Baiwei Guo ◽  
Orcun Karaca ◽  
Tyler H. Summers ◽  
Maryam Kamgarpour
Keyword(s):  
Greedy Algorithms ◽  
Structural Controllability ◽  
Actuator Placement

A class of iterative greedy algorithms related to Blaschke product

2021 ◽  
Author(s):  
Tao Qian ◽  
Lihui Tan ◽  
Jiecheng Chen
Keyword(s):  
Blaschke Product ◽  
Greedy Algorithms

scholarly journals Indication of safe transition paths of phase shifter settings by greedy algorithms

2007 ◽  
Author(s):  
J. Verboomen ◽  
D. Van Hertem ◽  
P. H. Schavemaker ◽  
W. L. Kling ◽  
R. Belmans
Keyword(s):  
Phase Shifter ◽  
Greedy Algorithms ◽  
Transition Paths

Heterogeneous Influence Maximization Through Community Detection in Social Networks

2021 ◽  
Vol 12 (4) ◽  
pp. 118-131
Author(s):  
Jaya Krishna Raguru ◽  
Devi Prasad Sharma
Keyword(s):  
Community Detection ◽  
Greedy Algorithms ◽  
Computational Cost ◽  
Optimal Solution ◽  
Influence Maximization ◽  
Centrality Measures ◽  
Influence Spread ◽  
Real World Datasets ◽  
Initial Seed ◽  
High Computational Cost

The problem of identifying a seed set composed of K nodes that increase influence spread over a social network is known as influence maximization (IM). Past works showed this problem to be NP-hard and an optimal solution to this problem using greedy algorithms achieved only 63% of spread. However, this approach is expensive and suffered from performance issues like high computational cost. Furthermore, in a network with communities, IM spread is not always certain. In this paper, heterogeneous influence maximization through community detection (HIMCD) algorithm is proposed. This approach addresses initial seed nodes selection in communities using various centrality measures, and these seed nodes act as sources for influence spread. A parallel influence maximization is applied with the aid of seed node set contained in each group. In this approach, graph is partitioned and IM computations are done in a distributed manner. Extensive experiments with two real-world datasets reveals that HCDIM achieves substantial performance improvement over state-of-the-art techniques.

scholarly journals On the convergence of greedy algorithms for initial segments of the Haar basis

2010 ◽  
Vol 148 (3) ◽  
pp. 519-529 ◽  
Author(s):  
S. J. DILWORTH ◽  
E. ODELL ◽  
TH. SCHLUMPRECHT ◽  
ANDRÁS ZSÁK
Keyword(s):  
Banach Space ◽  
Greedy Algorithm ◽  
General Result ◽  
Initial Segment ◽  
Greedy Algorithms ◽  
Dimensional Banach Space ◽  
Haar Basis ◽  
Finite Dimensional ◽  
Finite Dimensional Banach Space ◽  
Number Of Iterations

AbstractWe consider the X-Greedy Algorithm and the Dual Greedy Algorithm in a finite-dimensional Banach space with a strictly monotone basis as the dictionary. We show that when the dictionary is an initial segment of the Haar basis in Lp[0, 1] (1 < p < ∞) then the algorithms terminate after finitely many iterations and that the number of iterations is bounded by a function of the length of the initial segment. We also prove a more general result for a class of strictly monotone bases.

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