Edit distance is a measurement of similarity between two sequences such as strings, point sequences, or polygonal curves. Many matching problems from a variety of areas, such as signal analysis, bioinformatics, etc., need to be solved in a geometric space. Therefore, the geometric edit distance (GED) has been studied. In this paper, we describe the first strictly sublinear approximate near-linear time algorithm for computing the GED of two point sequences in constant dimensional Euclidean space. Specifically, we present a randomized (O(n\log^2n)) time (O(\sqrt n))-approximation algorithm. Then, we generalize our result to give a randomized \alpha-approximation algorithm for any \alpha\in [ 1, \sqrt{n, n / \log n} ], running in time  (n^2/\alpha^2  ). Both algorithms are Monte Carlo and return approximately optimal solutions with high probability.
Edit distance is a measurement of similarity between two sequences such as strings, point sequences, or polygonal curves. Many matching problems from a variety of areas, such as signal analysis, bioinformatics, etc., need to be solved in a geometric space. Therefore, the geometric edit distance (GED) has been studied. In this paper, we describe the first strictly sublinear approximate near-linear time algorithm for computing the GED of two point sequences in constant dimensional Euclidean space. Specifically, we present a randomized (O(n\log^2n)) time (O(\sqrt n))-approximation algorithm. Then, we generalize our result to give a randomized \alpha-approximation algorithm for any \alpha\in [ \sqrt{\log n, n / \log n} ], running in time O (n^2/\alpha^2 \log n ). Both algorithms are Monte Carlo and return approximately optimal solutions with high probability.
R
1, \sqrt{n
\sqrt{\log n
A
O
A
\log n