Turnpike with Uncertain Measurements: Triangle-Equality ILP with a Deterministic Recovery Guarantee

TL;DR

This paper introduces a new ILP-based method for reconstructing one-dimensional point sets from noisy, rounded distance measurements, providing a deterministic guarantee for exact recovery under certain conditions.

Contribution

It offers a combinatorial characterization of realizability and a novel ILP/LP approach with a deterministic recovery guarantee for uncertain measurements.

Findings

Exact recovery under bounded noise and rounding conditions.

The ILP/LP approach matches noiseless case combinatorial input.

Experimental validation of integrality and robustness.

Abstract

We study Turnpike with uncertain measurements: reconstructing a one-dimensional point set from an unlabeled multiset of pairwise distances under bounded noise and rounding. We give a combinatorial characterization of realizability via a multi-matching that labels interval indices by distinct distance values while satisfying all triangle equalities. This yields an ILP based on the triangle equality whose constraint structure depends only on the two-partition set $\mathcal{P}_y=\{(r,s,t): y_r+y_s=y_t\}$ and a natural LP relaxation with $\{0,1\}$-coefficient constraints. Integral solutions certify realizability and output an explicit assignment matrix, enabling an assignment-first, regression-second pipeline for downstream coordinate estimation. Under bounded noise followed by rounding, we prove a deterministic separation condition under which $\mathcal{P}_y$ is recovered exactly, so the ILP/LP receives the same combinatorial input as in the noiseless case. Experiments illustrate integrality behavior and degradation outside the provable regime.