A set function on a ground set of size n is approximately modular if it satisfies every modularity requirement to within an additive error, approximate modularity is the set analog of approximate linearity. In this paper we study how close, in additive error, can approximately modular functions be to truly modular functions. We first obtain a polynomial time algorithm that makes O(n2 log n) queries to any approximately modular function to reconstruct a modular function that is O(√n)-close. We also show an almost matching lower bound: any algorithm world need super polynomially many queries to construct a modular function that is O(√n/log n)-close. In a striking contrast to these near-tight computational reconstruction bounds, we then show that for any approximately modular function, there exists a modular function that is O(log n)-close.
Approximate modularity / Chierichetti, Flavio; Das, Abhimanyu; Dasgupta, Anirban; Kumar, Ravi. - 2015-December:(2015), pp. 1143-1162. (Intervento presentato al convegno 56th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2015 tenutosi a Berkeley; United States nel 2015) [10.1109/FOCS.2015.74].
Approximate modularity
CHIERICHETTI, FLAVIO;
2015
Abstract
A set function on a ground set of size n is approximately modular if it satisfies every modularity requirement to within an additive error, approximate modularity is the set analog of approximate linearity. In this paper we study how close, in additive error, can approximately modular functions be to truly modular functions. We first obtain a polynomial time algorithm that makes O(n2 log n) queries to any approximately modular function to reconstruct a modular function that is O(√n)-close. We also show an almost matching lower bound: any algorithm world need super polynomially many queries to construct a modular function that is O(√n/log n)-close. In a striking contrast to these near-tight computational reconstruction bounds, we then show that for any approximately modular function, there exists a modular function that is O(log n)-close.File | Dimensione | Formato | |
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