Measuring sequence similarity and compressing texts are among the most fundamental
tasks in string algorithms. In this work, we develop near-optimal quantum algorithms for the …

The edit distance is a fundamental measure of sequence similarity, defined as the minimum
number of character insertions, deletions, and substitutions needed to transform one string …

The edit distance (also known as Levenshtein distance) of two strings is the minimum
number of insertions, deletions, and substitutions of characters needed to transform one …

We study the problem of approximating edit distance in sublinear time. This is formalized as
the (k,\k^c)-GAP EDIT DISTANCE problem, where the input is a pair of strings X,Y and …

Given a database of bit strings $ A_1,\ldots, A_m\in\{0, 1\}^ n $, a fundamental data structure
task is to estimate the distances between a given query $ B\in\{0, 1\}^ n $ with all the strings …

The edit distance of two strings is the minimum number of insertions, deletions, and
substitutions needed to transform one string into the other. The textbook algorithm …

In this PhD thesis on fine-grained algorithm design and complexity, we investigate output-
sensitive and sublinear-time algorithms for two important problems.(1) Sparse Convolution …

The edit distance between strings classically assigns unit cost to every character insertion,
deletion, and substitution, whereas the Hamming distance only allows substitutions. In many …

We study the fundamental problem of approximating the edit distance of two strings. After an
extensive line of research led to the development of a constant-factor approximation …