A brief note on approximate optimization of submodular functions
Alen Alexanderian
TL;DR
This paper discusses the greedy algorithm and its variants for efficiently approximating the maximization of monotone submodular functions, highlighting their effectiveness and potential improvements.
Contribution
It provides a concise overview of the greedy method and introduces more efficient variants for approximate submodular function maximization.
Findings
Greedy algorithms are effective for submodular maximization.
Variants can improve efficiency of the greedy approach.
The methods are suitable for large-scale optimization tasks.
Abstract
We briefly discuss the greedy method and a couple of its more efficient variants for approximately maximizing monotone submodular functions.
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Optimization and Variational Analysis · Stochastic Gradient Optimization Techniques