Permutation Matching Under Parikh Budgets: Linear-Time Detection, Packing, and Disjoint Selection

TL;DR

This paper introduces efficient linear-time algorithms for permutation pattern matching, packing, and disjoint selection based on Parikh vector differences, connecting string matching with packing optimization.

Contribution

It presents a unified sliding-window framework for permutation matching and introduces a novel packing variant, MFSP, with linear-time solutions and a greedy approach for disjoint match selection.

Findings

Permutation matching achieved in O(n + σ) time and space.

MFSP problem solved efficiently with a two-pointer algorithm.

Disjoint occurrence selection can be optimized with a greedy strategy.

Abstract

We study permutation (jumbled/Abelian) pattern matching over a general alphabet $\Sigma$. Given a pattern P of length m and a text T of length n, the classical task is to decide whether T contains a length-m substring whose Parikh vector equals that of P . While this existence problem admits a linear-time sliding-window solution, many practical applications require optimization and packing variants beyond mere detection. We present a unified sliding-window framework based on maintaining the Parikh-vector difference between P and the current window of T , enabling permutation matching in O(n + {\sigma}) time and O({\sigma}) space, where {\sigma} = |{\Sigma}|. Building on this foundation, we introduce a combinatorial-optimization variant that we call Maximum Feasible Substring under Pattern Supply (MFSP): find the longest substring S of T whose symbol counts are component-wise bounded by those of P . We show that MFSP can also be solved in O(n + {\sigma}) time via a two-pointer feasibility maintenance algorithm, providing an exact packing interpretation of P as a resource budget. Finally, we address non-overlapping occurrence selection by modeling each permutation match as an equal-length interval and proving that a greedy earliest-finishing strategy yields a maximum-cardinality set of disjoint matches, computable in linear time once all matches are enumerated. Our results provide concise, provably correct algorithms with tight bounds, and connect frequency-based string matching to packing-style optimization primitives.