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Electronic Colloquium on Computational Complexity

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REPORTS > AUTHORS > TOMER GROSSMAN:
All reports by Author Tomer Grossman:

TR21-152 | 8th November 2021
Gal Arnon, Tomer Grossman

Min-Entropic Optimality

We introduce the notion of \emph{Min-Entropic Optimality} thereby providing a framework for arguing that a given algorithm computes a function better than any other algorithm. An algorithm is $k(n)$ Min-Entropic Optimal if for every distribution $D$ with min-entropy at least $k(n)$, its expected running time when its input is drawn ... more >>>


TR19-113 | 5th September 2019
Tomer Grossman, Ilan Komargodski, Moni Naor

Instance Complexity and Unlabeled Certificates in the Decision Tree Model

Instance complexity is a measure of goodness of an algorithm in which the performance of one algorithm is compared to others per input. This is in sharp contrast to worst-case and average-case complexity measures, where the performance is compared either on the worst input or on an average one, ... more >>>




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