NP-Hardness and a PTAS for the Pinwheel Problem

📅 2026-04-15
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🤖 AI Summary
This study addresses the computational complexity and approximation algorithms for the Pinwheel scheduling problem. By constructing a polynomial-time reduction from a known NP-hard problem, it rigorously establishes for the first time that Pinwheel scheduling is NP-hard, thereby implying the NP-hardness of several related periodic scheduling problems. Building on this hardness result, the paper presents a polynomial-time approximation scheme (PTAS) that surpasses the previously best-known approximation ratio of 9/7, achieving the current state-of-the-art approximation guarantee. This work not only settles the theoretical complexity status of the Pinwheel problem but also provides an efficient approximation framework for practical periodic task scheduling applications.

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📝 Abstract
In the pinwheel problem, one is given an $m$-tuple of positive integers $(a_1, \ldots, a_m)$ and asked whether the integers can be partitioned into $m$ color classes $C_1,\ldots,C_m$ such that every interval of length $a_i$ has non-empty intersection with $C_i$, for $i = 1, 2, \ldots, m$. It was a long-standing open question whether the pinwheel problem is NP-hard. We affirm a prediction of Holte et al. (1989) by demonstrating, for the first time, NP-hardness of the pinwheel problem. This enables us to prove NP-hardness for a host of other problems considered in the literature: pinwheel covering, bamboo garden trimming, windows scheduling, recurrent scheduling, and the constant gap problem. On the positive side, we develop a PTAS for an approximate version of the pinwheel problem. Previously, the best approximation factor known to be achievable in polynomial time was $\frac{9}{7}$.
Problem

Research questions and friction points this paper is trying to address.

Pinwheel Problem
NP-Hardness
Scheduling
Computational Complexity
Approximation Algorithms
Innovation

Methods, ideas, or system contributions that make the work stand out.

NP-hardness
PTAS
Pinwheel Problem
Approximation Algorithm
Scheduling
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