Results 131 to 140 of about 1,516 (207)

Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization [PDF]

Constrained submodular optimization problems play a key role in the area of combinatorial optimization as they capture many NP-hard optimization problems.
Neumann, Frank, Rudolph, Günter
core   +1 more source

Choosing panels of genomics assays using submodular optimization. [PDF]

Genome Biol, 2016
Wei K, Libbrecht MW, Bilmes JA, Noble WS.   +3 more
europepmc   +1 more source

Hypergraphs with edge-dependent vertex weights: p-Laplacians and spectral clustering. [PDF]

Front Big Data, 2023
Zhu Y, Segarra S.
europepmc   +1 more source

Submodular Order Functions and Assortment Optimization

Management Science
We define a new class of set functions that, in addition to being monotone and subadditive, also admit a very limited form of submodularity defined over a permutation of the ground set. We refer to this permutation as a submodular order. This class of functions includes monotone submodular functions as a subfamily.
openaire   +3 more sources

Ranking with submodular functions on a budget. [PDF]

Data Min Knowl Discov, 2022
Zhang G, Tatti N, Gionis A.
europepmc   +1 more source

Zeroth-order Stochastic Approximation Algorithms for DR-submodular Optimization [PDF]

In this paper, we study approximation algorithms for several classes of DR-submodular optimization problems, where DR is short for diminishing return. Following a newly introduced algorithm framework for zeroth-order stochastic approximation methods, we ...
Xu, Dachuan, Zhao, Zhongrui, Lian, Yuefang, Wang, Xiao   +3 more
core  

The Ground-Set-Cost Budgeted Maximum Coverage Problem. [PDF]

Theory Comput Syst
van Heuven van Staereling I, de Keijzer B, Schäfer G.   +2 more
europepmc   +1 more source

On the firefighter problem with spreading vaccination for maximizing the number of saved nodes: the IP model and LP rounding algorithms. [PDF]

Optim Lett, 2022
Yang Y, Chen PA, Lee YC, Fanchiang YY.
europepmc   +1 more source

A deep reinforcement learning framework for influence maximization problem on large-scale social networks. [PDF]

Sci Rep
Yang F, Wang Y, Shu N, Du X, Teng X, Liu J.   +5 more
europepmc   +1 more source

Maximum independent set in multiplex social networks and its application to influence maximization. [PDF]

Sci Rep
Daliri Khomami MM, Rezvanian A, Meybodi MR.   +2 more
europepmc   +1 more source