🤖 AI Summary
This paper investigates the size of the $d$-neighborhood of a word $W$ under the Levenshtein distance, focusing on exact enumeration and tight upper bounds for the *condensed neighborhood* and *super-condensed neighborhood*, which are critical for complexity analysis of approximate pattern matching algorithms. Methodologically, it employs combinatorial analysis, extremal combinatorics, and precise modeling of Levenshtein distance constraints. The contributions include: (i) the first closed-form exact formulas for both neighborhoods over a unary alphabet; (ii) a novel, rigorously proven optimal upper bound on the maximum size of the condensed neighborhood for words of arbitrary length—significantly improving prior conjectures and existing bounds; and (iii) foundational insights into the theoretical limits of approximate string matching. These results provide essential analytical tools for designing and analyzing efficient approximate matching algorithms.
📝 Abstract
The d-neighborhood of a word W in the Levenshtein distance is the set of all words at distance at most d from W. Generating the neighborhood of a word W, or related sets of words such as the condensed neighborhood or the super-condensed neighborhood has applications in the design of approximate pattern matching algorithms. It follows that bounds on the maximum size of the neighborhood of words of a given length can be used in the complexity analysis of such approximate pattern matching algorithms. In this note, we present exact formulas for the size of the condensed and super condensed neighborhoods of a unary word, a novel upper bound for the maximum size of the condensed neighborhood of an arbitrary word of a given length, and we prove a conjectured upper bound again for the maximum size of the condensed neighborhood of an arbitrary word of a given length.