Bounding extrema over global attractors using polynomial optimisation
David Goluskin

TL;DR
This paper introduces a computational framework using polynomial optimisation and Lyapunov functions to bound extreme values on global attractors of polynomial dynamical systems, demonstrated through chaotic and stable systems.
Contribution
It develops a novel method combining polynomial optimisation and Lyapunov functions to compute bounds on extrema over attractors, including non-global regions, with practical computational implementation.
Findings
Successfully bounded extreme values in Lorenz system
Bounded extrema in fluid dynamics truncation with chaotic transients
Constructed bounds for basins of attraction with polynomial Lyapunov functions
Abstract
We describe a framework for bounding extreme values of quantities on global attractors of differential dynamical systems. A global attractor is the minimal set that attracts all bounded sets; it contains all forward-time limit points. Our approach uses (generalised) Lyapunov functions to find attracting sets, which must contain the global attractor, and the choice of Lyapunov function is optimised based on the quantity whose extreme value one aims to bound. We also present a non-global framework for bounding extrema over the minimal set that is attracting in a specified region of state space. If the dynamics are governed by ordinary differential equations, and the equations and quantities of interest are polynomial, then our methods can be implemented computationally using polynomial optimisation. In particular, we enforce nonnegativity of certain polynomial expressions by requiring…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
