Results 101 to 110 of about 1,909,109 (209)

Submodular Functions: from Discrete to Continous Domains [PDF]

, 2018
International audienceSubmodular set-functions have many applications in combinatorial optimization, as they can be minimized and approximately maximized in polynomial time.
Bach, Francis, Francis Bach
core   +1 more source

The Adaptive Complexity of Submodular Optimization

, 2019
In this thesis, we develop a new optimization technique that leads to exponentially faster algorithms for solving submodular optimization problems.
Balkanski, Eric
core   +1 more source

Multipass Streaming Algorithms for Regularized Submodular Maximization

Tsinghua Science and Technology
In this work, we study a k-Cardinality Constrained Regularized Submodular Maximization (k-CCRSM) problem, in which the objective utility is expressed as the difference between a non-negative submodular and a modular function.
Qinqin Gong, Suixiang Gao, Fengmin Wang, Ruiqi Yang   +3 more
doaj   +1 more source

Submodularization for Quadratic Pseudo-Boolean Optimization

CoRR, 2013
8 pages, 5 figures, to appear at IEEE conference on Computer Vision and Pattern Recognition (CVPR), June ...
Lena Gorelick, Yuri Boykov, Olga Veksler, Ismail Ben Ayed, Andrew Delong   +4 more
openaire   +3 more sources

Submodular Selection of Assays v0.1

, 2016
<p>Submodular-Selection-of-Assays (SSA)</p> <p>Please see the following manuscript for more details: Kai Wei * , Maxwell W. Libbrecht * , Jeffrey A Bilmes, William S. Noble.
Wei, Kai, Bilmes, Jeffrey A (5275519), Libbrecht, Maxwell, Wei, Kai (5275525), Libbrecht, Maxwell (5275528), Noble, William S (5275522), Bilmes, Jeffrey A, Noble, William S   +7 more
core   +1 more source

Submodular Optimization over Streams with Inhomogeneous Decays

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Cardinality constrained submodular function maximization, which aims to select a subset of size at most k to maximize a monotone submodular utility function, is the key in many data mining and machine learning applications such as data summarization and maximum coverage problems.
Junzhou Zhao, Shuo Shang, Pinghui Wang, John C. S. Lui, Xiangliang Zhang 0001   +4 more
openaire   +5 more sources

A hierarchical evolutionary deep reinforcement learning algorithm for influence maximization in multilayer hypergraphs

Frontiers in Physics
Multilayer hypergraphs provide a unified representation of influence diffusion in complex networks by coupling relation layers through shared entities and encoding higher-order group interactions as hyperedges.
Yanan Zhang, Yufeng Su, Yunfan Zhou, Yao Yue, Zhen Li, Zhi Wu   +5 more
doaj   +1 more source

Data Correcting Algorithms in Combinatorial Optimization [PDF]

This paper describes data correcting algorithms. It provides the theory behind the algorithms and presents the implementation details and computational experience with these algorithms on the asymmetric traveling salesperson problem, the problem of ...
Goldengorin, Boris, Sierksma Gerard, Ghosh, Diptesh   +2 more
core  

Queue-Aware Cell Activation and User Association for Traffic Offloading via Dual-Connectivity

IEEE Access, 2019
With the objective of reducing energy cost, we study the stochastic optimization of traffic off-loading via dual-connectivity by joint cell activation and user association.
Qiaoni Han, Bo Yang, Xiaocheng Wang
doaj   +1 more source

Low-Delay and High-Coverage Water Distribution Networks Monitoring Using Mobile Sensors

IEEE Access, 2019
Urban water distribution networks (WDNs) are usually threatened by leakage, reflux, infiltration and internal pollution. To ensure the safety of water supply, it is essential to properly monitor the WDNs.
Junbin Liang, Xueyue Xue, Zongjian He, Victor C. M. Leung   +3 more
doaj   +1 more source