We investigate the problem of maximizing the sum of submodular and supermodular functions under a fairness constraint.This sum function is non-submodular in general.For an offline model,we introduce two approximation ...We investigate the problem of maximizing the sum of submodular and supermodular functions under a fairness constraint.This sum function is non-submodular in general.For an offline model,we introduce two approximation algorithms:A greedy algorithm and a threshold greedy algorithm.For a streaming model,we propose a one-pass streaming algorithm.We also analyze the approximation ratios of these algorithms,which all depend on the total curvature of the supermodular function.The total curvature is computable in polynomial time and widely utilized in the literature.展开更多
Social Influence Maximization Problems(SIMPs)deal with selecting k seeds in a given Online Social Network(OSN)to maximize the number of eventually-influenced users.This is done by using these seeds based on a given se...Social Influence Maximization Problems(SIMPs)deal with selecting k seeds in a given Online Social Network(OSN)to maximize the number of eventually-influenced users.This is done by using these seeds based on a given set of influence probabilities among neighbors in the OSN.Although the SIMP has been proved to be NP-hard,it has both submodular(with a natural diminishing-return)and monotone(with an increasing influenced users through propagation)that make the problem suitable for approximation solutions.However,several special SIMPs cannot be modeled as submodular or monotone functions.In this paper,we look at several conditions under which non-submodular or non-monotone functions can be handled or approximated.One is a profit-maximization SIMP where seed selection cost is included in the overall utility function,breaking the monotone property.The other is a crowd-influence SIMP where crowd influence exists in addition to individual influence,breaking the submodular property.We then review several new techniques and notions,including double-greedy algorithms and the supermodular degree,that can be used to address special SIMPs.Our main results show that for a specific SIMP model,special network structures of OSNs can help reduce its time complexity of the SIMP.展开更多
In this paper, we study the dynamic facility location problem with submodular penalties （DFLPSP）. We present a combinatorial primal-dual 3-approximation algorithm for the DFLPSP.
The concept of linear tangle was introduced as an obstruction to mixed searching number. The concept of (maximal) single ideal has been introduced as an obstruction to linear-width. Moreover, it was already known that...The concept of linear tangle was introduced as an obstruction to mixed searching number. The concept of (maximal) single ideal has been introduced as an obstruction to linear-width. Moreover, it was already known that mixed search number is equivalent to linear-width. Hence, by combining those results, we obtain a proof of the equivalence between linear tangle and maximal single ideal. This short report gives an alternative proof of the equivalence.展开更多
In this paper,we consider the generalized prize-collecting Steiner forest problem with submodular penalties(GPCSF-SP problem).In this problem,we are given an undirected connected graph G=(V,E)and a collection of disjo...In this paper,we consider the generalized prize-collecting Steiner forest problem with submodular penalties(GPCSF-SP problem).In this problem,we are given an undirected connected graph G=(V,E)and a collection of disjoint vertex subsets V={V_(1),V_(2),…,V_(l)}.Assume c:E→R_(+)is an edge cost function andπ:2^(V)→R_(+)is a submodular penalty function.The objective of the GPCSF-SP problem is to find an edge subset F such that the total cost including the edge cost in F and the penalty cost of the subcollection S containing these Vi not connected by F is minimized.By using the primal-dual technique,we give a 3-approximation algorithm for this problem.展开更多
基金The first author was supported by the National Natural Science Foundation of China(Nos.12001025 and 12131003)The second author was supported by the Spark Fund of Beijing University of Technology(No.XH-2021-06-03)+2 种基金The third author was supported by the Natural Sciences and Engineering Research Council of Canada(No.283106)the Natural Science Foundation of China(Nos.11771386 and 11728104)The fourth author is supported by the National Natural Science Foundation of China(No.12001335).
文摘We investigate the problem of maximizing the sum of submodular and supermodular functions under a fairness constraint.This sum function is non-submodular in general.For an offline model,we introduce two approximation algorithms:A greedy algorithm and a threshold greedy algorithm.For a streaming model,we propose a one-pass streaming algorithm.We also analyze the approximation ratios of these algorithms,which all depend on the total curvature of the supermodular function.The total curvature is computable in polynomial time and widely utilized in the literature.
基金the National Science Foundation(NSF)grants Computer and Network Systems(CNS)1824440,CNS 1828363,CNS 1757533,CNS 1618398,CNS 1651947,and CNS 1564128。
文摘Social Influence Maximization Problems(SIMPs)deal with selecting k seeds in a given Online Social Network(OSN)to maximize the number of eventually-influenced users.This is done by using these seeds based on a given set of influence probabilities among neighbors in the OSN.Although the SIMP has been proved to be NP-hard,it has both submodular(with a natural diminishing-return)and monotone(with an increasing influenced users through propagation)that make the problem suitable for approximation solutions.However,several special SIMPs cannot be modeled as submodular or monotone functions.In this paper,we look at several conditions under which non-submodular or non-monotone functions can be handled or approximated.One is a profit-maximization SIMP where seed selection cost is included in the overall utility function,breaking the monotone property.The other is a crowd-influence SIMP where crowd influence exists in addition to individual influence,breaking the submodular property.We then review several new techniques and notions,including double-greedy algorithms and the supermodular degree,that can be used to address special SIMPs.Our main results show that for a specific SIMP model,special network structures of OSNs can help reduce its time complexity of the SIMP.
基金国家自然科学基金(6117212761401001)+4 种基金高等学校博士学科点专项科研基金(20113401110006)安徽省自然科学基金(1508085MF120)资助Supported by National Natural Science Foundation of China(6117212761401001)Specialized Research Fund for the Doctoral Program of Higher Education of China(20113401110006)and Anhui Provincial Natural Science Foundation(1508085MF120)
基金Supported in part by Hebei Province Department of Education Fund under Grant No.Z2012017the National Natural Science Foundation of China under Grant No.11371001 and 11201013
文摘In this paper, we study the dynamic facility location problem with submodular penalties （DFLPSP）. We present a combinatorial primal-dual 3-approximation algorithm for the DFLPSP.
文摘The concept of linear tangle was introduced as an obstruction to mixed searching number. The concept of (maximal) single ideal has been introduced as an obstruction to linear-width. Moreover, it was already known that mixed search number is equivalent to linear-width. Hence, by combining those results, we obtain a proof of the equivalence between linear tangle and maximal single ideal. This short report gives an alternative proof of the equivalence.
基金This work is supported by the National Natural Science Foundation of China(No.11971146)the Natural Science Foundation of Hebei Province(Nos.A2019205089 and A2019205092)+1 种基金Hebei Province Foundation for Returnees(No.CL201714)Overseas Expertise Introduction Program of Hebei Auspices(No.25305008).
文摘In this paper,we consider the generalized prize-collecting Steiner forest problem with submodular penalties(GPCSF-SP problem).In this problem,we are given an undirected connected graph G=(V,E)and a collection of disjoint vertex subsets V={V_(1),V_(2),…,V_(l)}.Assume c:E→R_(+)is an edge cost function andπ:2^(V)→R_(+)is a submodular penalty function.The objective of the GPCSF-SP problem is to find an edge subset F such that the total cost including the edge cost in F and the penalty cost of the subcollection S containing these Vi not connected by F is minimized.By using the primal-dual technique,we give a 3-approximation algorithm for this problem.
