$\texttt{immrax}$: A Parallelizable and Differentiable Toolbox for Interval Analysis and Mixed Monotone Reachability in JAX

Akash Harapanahalli, Saber Jafarpour, Samuel Coogan
1/21/2024
eess.SYcs.LGmath.OC

No official code found. Best implementation: gtfactslab/immrax (21 stars, last commit 2026-02-22).

Abstract

We present an implementation of interval analysis and mixed monotone interval reachability analysis as function transforms in Python, fully composable with the computational framework JAX. The resulting toolbox inherits several key features from JAX, including computational efficiency through Just-In-Time Compilation, GPU acceleration for quick parallelized computations, and Automatic Differentiability. We demonstrate the toolbox's performance on several case studies, including a reachability problem on a vehicle model controlled by a neural network, and a robust closed-loop optimal control problem for a swinging pendulum.

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Code Implementations(1)

gtfactslab/immraxCommunity100%

Interval Analysis and Mixed Monotone Reachability in JAX

216PythonNov 29, 20238 months ago

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Cite this paper

@article{harapanahalli2024textttimmrax,
  title  = {$\texttt{immrax}$: A Parallelizable and Differentiable Toolbox for Interval Analysis and Mixed Monotone Reachability in JAX},
  author = {Akash Harapanahalli and Saber Jafarpour and Samuel Coogan},
  year   = {2024},
  eprint = {2401.11608},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.11608v2}
}

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