LINPRO: linear inverse problem library for data contaminated by statistical noise

TL;DR

LINPRO is a library that solves linear inverse problems with noisy data using Maximum Entropy and SVD methods, demonstrated on quantum Monte Carlo data.

Contribution

It introduces LINPRO, a library implementing two specific methods for linear inverse problems with noisy data, applied to quantum Monte Carlo results.

Findings

Successful application to quantum Monte Carlo data

Effective use of Maximum Entropy and SVD methods

Provides a practical tool for inverse problems in physics

Abstract

The library LINPRO which provides solution to the linear inverse problem for data contaminated by a statistical noise is presented. The library makes use of two methods: Maximum Entropy Method and Singular Value Decomposition. As an example it has been applied to perform an analytic continuation of the imaginary time propagator obtained within the Quantum Monte Carlo method.