Pareto optimization for subset selection with dynamic cost constraints

We consider the subset selection problem for function f with constraint bound B that changes over time. Within the area of submodular optimization, various greedy approaches are commonly used. For dynamic environments we observe that the adaptive variants of these greedy approaches are not able to m...

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Vydáno v:Artificial intelligence Ročník 302; s. 103597
Hlavní autoři: Roostapour, Vahid, Neumann, Aneta, Neumann, Frank, Friedrich, Tobias
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.01.2022
Elsevier Science Ltd
Témata:
Algorithms
Approximation
Empirical analysis
Evolutionary algorithms
Greedy algorithms
Multi-objective optimization
Multiple objective analysis
Optimization
Pareto optimization
Polynomials
Runtime analysis
Social networks
Submodular function
Subset selection
Submodular function
Multi-objective optimization
Runtime analysis
Subset selection
ISSN:0004-3702, 1872-7921
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Shrnutí:We consider the subset selection problem for function f with constraint bound B that changes over time. Within the area of submodular optimization, various greedy approaches are commonly used. For dynamic environments we observe that the adaptive variants of these greedy approaches are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach, we show that this algorithm efficiently computes a ϕ=(αf/2)(1−1eαf)-approximation, where αf is the submodularity ratio of f, for each possible constraint bound b≤B. Furthermore, we show that POMC is able to adapt its set of solutions quickly in the case that B increases. Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms. We also consider EAMC, a new evolutionary algorithm with polynomial expected time guarantee to maintain ϕ approximation ratio, and NSGA-II with two different population sizes as advanced multi-objective optimization algorithm, to demonstrate their challenges in optimizing the maximum coverage problem. Our empirical analysis shows that, within the same number of evaluations, POMC is able to perform as good as NSGA-II under linear constraint, while EAMC performs significantly worse than all considered algorithms in most cases.
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ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2021.103597