# Ordered Line Integral Methods for Computing the Quasi-potential

## TL;DR

This paper introduces Ordered Line Integral Methods (OLIMs), a new family of algorithms for efficiently computing the quasi-potential in stochastic dynamical systems, offering significant speed and accuracy improvements over previous methods.

## Contribution

The paper presents OLIMs, which improve upon the Ordered Upwind Method by optimizing updates and using quadrature rules, resulting in faster and more accurate quasi-potential computations.

## Key findings

- OLIMs are 1.5 to 4 times faster than previous methods.
- OLIMs achieve error reductions of two to three orders of magnitude.
- Higher order quadrature rules significantly improve accuracy.

## Abstract

The quasi-potential is a key function in the Large Deviation Theory. It characterizes the difficulty of the escape from the neighborhood of an attractor of a stochastic non-gradient dynamical system due to the influence of small white noise. It also gives an estimate of the invariant probability distribution in the neighborhood of the attractor up { to} the exponential order. We present a new family of methods for computing the quasi-potential on a regular mesh named the Ordered Line Integral Methods (OLIMs). In comparison with the first proposed quasi-potential finder based on the Ordered Upwind Method (OUM) (Cameron, 2012), the new methods are 1.5 to 4 times faster, can produce error two to three orders of magnitude smaller, and may exhibit faster convergence. Similar to the OUM, OLIMs employ the dynamical programming principle. Contrary to it, they (i) have an optimized strategy for the use of computationally expensive { triangle} updates leading to a notable speed-up, and (ii) directly solve local minimization problems using quadrature rules instead of solving the corresponding Hamilton-Jacobi-type equation by the first order finite difference upwind scheme. The OLIM with the right-hand quadrature rule is equivalent to OUM. The use of higher order quadrature rules in local minimization problems dramatically boosts up the accuracy of OLIMs. We offer a detailed discussion on the origin of numerical errors in OLIMs and propose rules-of-thumb for the choice of the important parameter, the update factor, in the OUM and OLIMs. Our results are supported by extensive numerical tests on two challenging 2D examples.