Space efficient streaming algorithms for the distance to monotonicity and asymmetric edit distance

TL;DR

This paper introduces simple, space-efficient streaming algorithms for approximating the distance to monotonicity and asymmetric edit distance, achieving near-optimal accuracy with minimal memory usage.

Contribution

It presents novel streaming algorithms that approximate the distance to monotonicity and asymmetric LCS with significantly reduced space complexity and simplicity compared to prior methods.

Findings

Achieves a (1+δ)-approximation to the distance to monotonicity using O((log^2 n)/δ) space.

Provides an additive δn approximation for LIS length in streaming model.

Develops an asymmetric streaming algorithm for LCS with space O(k(log^2 n)/δ).

Abstract

Approximating the length of the longest increasing sequence (LIS) of an array is a well-studied problem. We study this problem in the data stream model, where the algorithm is allowed to make a single left-to-right pass through the array and the key resource to be minimized is the amount of additional memory used. We present an algorithm which, for any $\delta > 0$, given streaming access to an array of length $n$ provides a $(1+\delta)$-multiplicative approximation to the \emph{distance to monotonicity} ($n$ minus the length of the LIS), and uses only $O((\log^2 n)/\delta)$ space. The previous best known approximation using polylogarithmic space was a multiplicative 2-factor. Our algorithm can be used to estimate the length of the LIS to within an additive $\delta n$ for any $\delta >0$ while previous algorithms could only achieve additive error $n(1/2-o(1))$. Our algorithm is very simple, being just 3 lines of pseudocode, and has a small update time. It is essentially a polylogarithmic space approximate implementation of a classic dynamic program that computes the LIS. We also give a streaming algorithm for approximating $LCS(x,y)$, the length of the longest common subsequence between strings $x$ and $y$, each of length $n$. Our algorithm works in the asymmetric setting (inspired by \cite{AKO10}), in which we have random access to $y$ and streaming access to $x$, and runs in small space provided that no single symbol appears very often in $y$. More precisely, it gives an additive-$\delta n$ approximation to $LCS(x,y)$ (and hence also to $E(x,y) = n-LCS(x,y)$, the edit distance between $x$ and $y$ when insertions and deletions, but not substitutions, are allowed), with space complexity $O(k(\log^2 n)/\delta)$, where $k$ is the maximum number of times any one symbol appears in $y$.