Unified greedy approximability beyond submodular maximization
Yann Disser, David Weckbecker

TL;DR
This paper introduces a new class of objective functions called $oldsymbol{ ext{ extgamma}- ext{ extalpha}}$-augmentable functions, providing tight approximation bounds for greedy algorithms in cardinality constrained maximization problems, unifying and extending existing results.
Contribution
It defines the $ ext{ extgamma}- ext{ extalpha}$-augmentable class, proves tight bounds on greedy approximation ratios, and closes gaps in previous bounds for $ ext{ extalpha}$-augmentable functions and independence systems.
Findings
Introduces $ ext{ extgamma}- ext{ extalpha}$-augmentable functions class.
Provides tight approximation bounds for greedy algorithms.
Unifies bounds for various subclasses like submodular and independence systems.
Abstract
We consider classes of objective functions of cardinality constrained maximization problems for which the greedy algorithm guarantees a constant approximation. We propose the new class of --augmentable functions and prove that it encompasses several important subclasses, such as functions of bounded submodularity ratio, -augmentable functions, and weighted rank functions of an independence system of bounded rank quotient - as well as additional objective functions for which the greedy algorithm yields an approximation. For this general class of functions, we show a tight bound of on the approximation ratio of the greedy algorithm that tightly interpolates between bounds from the literature for functions of bounded submodularity ratio and for -augmentable functions. In paritcular, as…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Cryptography and Data Security
