activity
20162021
most citedStreaming Algorithms For Computing Edit Distance Without Exploiting Suffix Trees

9 citations · 10 across the 3 of their papers we have counts for

collaborators

6 papers

cs.DS2021

Does Preprocessing help in Fast Sequence Comparisons?

Elazar Goldenberg, Aviad Rubinstein, Barna Saha

We study edit distance computation with preprocessing: the preprocessing algorithm acts on each string separately, and then the query algorithm takes as input the two preprocessed…

cs.CC20191 cited

Sublinear Algorithms for Gap Edit Distance

Elazar Goldenberg, Robert Krauthgamer, Barna Saha

The edit distance is a way of quantifying how similar two strings are to one another by counting the minimum number of character insertions, deletions, and substitutions required t…

cs.CC2019

Hardness Amplification of Optimization Problems

Elazar Goldenberg, Karthik C. S.

In this paper, we prove a general hardness amplification scheme for optimization problems based on the technique of direct products. We say that an optimization problem is dire…

cs.CC2019

Towards a General Direct Product Testing Theorem

Elazar Goldenberg, Karthik C. S.

The Direct Product encoding of a string on an underlying domain , is a function DP which gets as input a set and outputs $…

cs.DS2018

Approximating Edit Distance Within Constant Factor in Truly Sub-Quadratic Time

Diptarka Chakraborty, Debarati Das, Elazar Goldenberg +2

Edit distance is a measure of similarity of two strings based on the minimum number of character insertions, deletions, and substitutions required to transform one string into the…

cs.DS20169 cited

Streaming Algorithms For Computing Edit Distance Without Exploiting Suffix Trees

Diptarka Chakraborty, Elazar Goldenberg, Michal Koucký

The edit distance is a way of quantifying how similar two strings are to one another by counting the minimum number of character insertions, deletions, and substitutions required t…