# Estimation of Monge Matrices

## TL;DR

This paper investigates the statistical estimation of Monge and pre-Monge matrices under noise, establishing minimax rates, proposing efficient estimators, and validating results through numerical experiments.

## Contribution

It introduces the first minimax analysis for Monge matrix estimation and proposes computationally efficient estimators for pre-Monge matrices.

## Key findings

- Established minimax rates for Monge and pre-Monge matrix estimation.
- Proposed two efficient estimators with proven convergence rates.
- Validated theoretical results with numerical experiments.

## Abstract

Monge matrices and their permuted versions known as pre-Monge matrices naturally appear in many domains across science and engineering. While the rich structural properties of such matrices have long been leveraged for algorithmic purposes, little is known about their impact on statistical estimation. In this work, we propose to view this structure as a shape constraint and study the problem of estimating a Monge matrix subject to additive random noise. More specifically, we establish the minimax rates of estimation of Monge and pre-Monge matrices. In the case of pre-Monge matrices, the minimax-optimal least-squares estimator is not efficiently computable, and we propose two efficient estimators and establish their rates of convergence. Our theoretical findings are supported by numerical experiments.