# Comparative Analysis of Barrier-like Function Methods for Reach-Avoid Verification in Stochastic Discrete-Time Systems

arXiv: https://arxiv.org/abs/2512.05348 · HTML: https://mlsanity.com/paper/2512.05348/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2025.

## Built on by (most cited first, 1 of 1)

- [Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning](https://mlsanity.com/paper/2605.11975/) (2026)

## Abstract (arXiv)

In this paper, we compare several representative barrier-like conditions from the literature for infinite-horizon reach-avoid verification of stochastic discrete-time systems. Our comparison examines both their theoretical properties and computational tractability, highlighting each condition's strengths and limitations that affect applicability and conservativeness. Finally, we illustrate their practical performance through computational experiments using semidefinite programming (SDP) and counterexample-guided inductive synthesis (CEGIS).