An Efficient Bounds Consistency Algorithm for the Global Cardinality Constraint

Constraints ◽  
2005 ◽  
Vol 10 (2) ◽  
pp. 115-135 ◽  
Author(s):  
Claude-Guy Quimper ◽  
Alexander Golynski ◽  
Alejandro López-Ortiz ◽  
Peter Van Beek
Keyword(s):  
Cardinality Constraint

Nonsubmodular Constrained Profit Maximization in Attribute Networks

2021 ◽  
Author(s):  
Liman Du ◽  
Wenguo Yang ◽  
Suixiang Gao
Keyword(s):  
Profit Maximization ◽  
Logistic Function ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Cardinality Constraint ◽  
Three Phase ◽  
Marginal Increment ◽  
The Difference ◽  
Classical Influence ◽  
Influence Maximization Problem

The number of social individuals who interact with their friends through social networks is increasing, leading to an undeniable fact that word-of-mouth marketing has become one of the useful ways to promote sale of products. The Constrained Profit Maximization in Attribute network (CPMA) problem, as an extension of the classical influence maximization problem, is the main focus of this paper. We propose the profit maximization in attribute network problem under a cardinality constraint which is closer to the actual situation. The profit spread metric of CPMA calculates the total benefit and cost generated by all the active nodes. Different from the classical Influence Maximization problem, the influence strength should be recalculated according to the emotional tendency and classification label of nodes in attribute networks. The profit spread metric is no longer monotone and submodular in general. Given that the profit spread metric can be expressed as the difference between two submodular functions and admits a DS decomposition, a three-phase algorithm named as Marginal increment and Community-based Prune and Search(MCPS) Algorithm frame is proposed which is based on Louvain algorithm and logistic function. Due to the method of marginal increment, MPCS algorithm can compute profit spread more directly and accurately. Experiments demonstrate the effectiveness of MCPS algorithm.

An Attribute Mapping Technique for Secure Interoperation in Multi-Domain Environments

2014 ◽  
Vol 519-520 ◽  
pp. 181-184
Author(s):  
Jian Feng Lu ◽  
Xuan Yan ◽  
Yi Ding Liu
Keyword(s):  
Access Control ◽  
Mapping Technique ◽  
Cardinality Constraint ◽  
Constraint Violation ◽  
Basic Technique ◽  
Access Control Policies ◽  
Role Mapping ◽  
Simple Action ◽  
Definition Of ◽  
Secure Interoperation

Role mapping is a basic technique for facilitating interoperation in RBAC-based collaborating environments. However, role mapping lacks the flexibility to specify access control policies in the scenarios where the access control is not a simple action, but consists of a sequence of actions and events from subjects and system. In this paper, we propose an attribute mapping technique to establish secure context in multi-domain environments. We first classify attributes into eight types and show that only two types of attributes need to be translated. We second give the definition of attribute mapping technique, and analysis the properties of attribute mapping. Finally, we study how cardinality constraint violation arises and shows that it is efficient to resolve this security violation.

scholarly journals Online Portfolio Selection with Cardinality Constraint and Transaction Costs based on Contextual Bandit

2020 ◽  
Author(s):  
Mengying Zhu ◽  
Xiaolin Zheng ◽  
Yan Wang ◽  
Qianqiao Liang ◽  
Wenfang Zhang
Keyword(s):  
Financial Markets ◽  
Transaction Costs ◽  
Portfolio Selection ◽  
Financial Engineering ◽  
Side Information ◽  
Cardinality Constraint ◽  
Real World Datasets ◽  
Online Portfolio ◽  
Online Portfolio Selection ◽  
Practical Constraints

Online portfolio selection (OLPS) is a fundamental and challenging problem in financial engineering, which faces two practical constraints during the real trading, i.e., cardinality constraint and non-zero transaction costs. In order to achieve greater feasibility in financial markets, in this paper, we propose a novel online portfolio selection method named LExp4.TCGP with theoretical guarantee of sublinear regret to address the OLPS problem with the two constraints. In addition, we incorporate side information into our method based on contextual bandit, which further improves the effectiveness of our method. Extensive experiments conducted on four representative real-world datasets demonstrate that our method significantly outperforms the state-of-the-art methods when cardinality constraint and non-zero transaction costs co-exist.

Two-stage submodular maximization problem beyond nonnegative and monotone

2021 ◽  
pp. 1-16
Author(s):  
Zhicheng Liu ◽  
Hong Chang ◽  
Ran Ma ◽  
Donglei Du ◽  
Xiaoyan Zhang
Keyword(s):  
Approximation Algorithms ◽  
Approximation Algorithm ◽  
Objective Function ◽  
Modular Function ◽  
Submodular Function ◽  
Maximization Problem ◽  
Cardinality Constraint ◽  
Two Stage ◽  
Time Efficiency ◽  
Submodular Maximization

Abstract We consider a two-stage submodular maximization problem subject to a cardinality constraint and k matroid constraints, where the objective function is the expected difference of a nonnegative monotone submodular function and a nonnegative monotone modular function. We give two bi-factor approximation algorithms for this problem. The first is a deterministic $\left( {{1 \over {k + 1}}\left( {1 - {1 \over {{e^{k + 1}}}}} \right),1} \right)$ -approximation algorithm, and the second is a randomized $\left( {{1 \over {k + 1}}\left( {1 - {1 \over {{e^{k + 1}}}}} \right) - \varepsilon ,1} \right)$ -approximation algorithm with improved time efficiency.

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