Adaptive Parameter Tuning for Reachability Analysis of Linear Systems

TL;DR

This paper introduces an automatic parameter tuning framework for reachability analysis of linear systems, making the process more accessible and efficient by eliminating manual parameter setting and optimizing runtime performance.

Contribution

The paper presents a generic, runtime parameter tuning framework that improves the efficiency and usability of reachability analysis for linear systems.

Findings

Faster verification on benchmarks compared to manual tuning.

Significant speed-up over genetic algorithms.

Applicable to large-scale linear systems with minimal expert input.

Abstract

Despite the possibility to quickly compute reachable sets of large-scale linear systems, current methods are not yet widely applied by practitioners. The main reason for this is probably that current approaches are not push-button-capable and still require to manually set crucial parameters, such as time step sizes and the accuracy of the used set representation -- these settings require expert knowledge. We present a generic framework to automatically find near-optimal parameters for reachability analysis of linear systems given a user-defined accuracy. To limit the computational overhead as much as possible, our methods tune all relevant parameters during runtime. We evaluate our approach on benchmarks from the ARCH competition as well as on random examples. Our results show that our new framework verifies the selected benchmarks faster than manually-tuned parameters and is an order of magnitude faster compared to genetic algorithms.