Tight Conditional Lower Bounds for Longest Common Increasing Subsequence

TL;DR

This paper establishes tight conditional lower bounds for the Longest Common Increasing Subsequence (LCIS) problem and its generalizations, showing no significantly faster algorithms exist under the Strong Exponential Time Hypothesis.

Contribution

It proves that LCIS and k-LCIS cannot be solved in strongly subquadratic or subexponential time, providing the first tight lower bounds for these problems based on SETH.

Findings

No strongly subquadratic algorithms for LCIS under SETH.

Lower bounds extend to k-LCIS and related problems.

Bounds follow from weaker variants of SETH.

Abstract

We consider the canonical generalization of the well-studied Longest Increasing Subsequence problem to multiple sequences, called $k$-LCIS: Given $k$ integer sequences $X_1,\dots,X_k$ of length at most $n$, the task is to determine the length of the longest common subsequence of $X_1,\dots,X_k$ that is also strictly increasing. Especially for the case of $k=2$ (called LCIS for short), several algorithms have been proposed that require quadratic time in the worst case. Assuming the Strong Exponential Time Hypothesis (SETH), we prove a tight lower bound, specifically, that no algorithm solves LCIS in (strongly) subquadratic time. Interestingly, the proof makes no use of normalization tricks common to hardness proofs for similar problems such as LCS. We further strengthen this lower bound (1) to rule out $O((nL)^{1-\varepsilon})$ time algorithms for LCIS, where $L$ denotes the solution size, (2) to rule out $O(n^{k-\varepsilon})$ time algorithms for $k$-LCIS, and (3) to follow already from weaker variants of SETH. We obtain the same conditional lower bounds for the related Longest Common Weakly Increasing Subsequence problem.