Dynamic Longest Increasing Subsequence and the Erd\"{o}s-Szekeres Partitioning Problem

TL;DR

This paper introduces new approximation algorithms for dynamic LIS and DTM problems, achieving near-optimal runtimes and improving solutions for the Erdös-Szekeres partitioning problem, with applications in online sequence analysis.

Contribution

The paper presents the first $(1+ ext{epsilon})$-approximation for DTM with polylogarithmic update time and a constant-factor approximation for LIS with nearly linear runtime, advancing dynamic sequence algorithms.

Findings

Achieved a $(1+ ext{epsilon})$-approximation for DTM with polylogarithmic worst-case update time.

Developed a constant-factor approximation algorithm for LIS with runtime $ ilde O(n^ ext{epsilon})$.

Provided an almost optimal dynamic algorithm for Erdös-Szekeres partitioning with runtime $ ilde O_ ext{epsilon}(n^{1+ ext{epsilon}})$.

Abstract

In this paper, we provide new approximation algorithms for dynamic variations of the longest increasing subsequence (\textsf{LIS}) problem, and the complementary distance to monotonicity (\textsf{DTM}) problem. In this setting, operations of the following form arrive sequentially: (i) add an element, (ii) remove an element, or (iii) substitute an element for another. At every point in time, the algorithm has an approximation to the longest increasing subsequence (or distance to monotonicity). We present a $(1+\epsilon)$-approximation algorithm for \textsf{DTM} with polylogarithmic worst-case update time and a constant factor approximation algorithm for \textsf{LIS} with worst-case update time $\tilde O(n^\epsilon)$ for any constant $\epsilon > 0$.% $n$ in the runtime denotes the size of the array at the time the operation arrives. Our dynamic algorithm for \textsf{LIS} leads to an almost optimal algorithm for the Erd\"{o}s-Szekeres partitioning problem. Erd\"{o}s-Szekeres partitioning problem was introduced by Erd\"{o}s and Szekeres in 1935 and was known to be solvable in time $O(n^{1.5}\log n)$. Subsequent work improve the runtime to $O(n^{1.5})$ only in 1998. Our dynamic \textsf{LIS} algorithm leads to a solution for Erd\"{o}s-Szekeres partitioning problem with runtime $\tilde O_{\epsilon}(n^{1+\epsilon})$ for any constant $\epsilon > 0$.