Sequential sum-of-squares programming for analysis of nonlinear systems

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Publication date: 5 October 2022

Abstract: Numerous interesting properties in nonlinear systems analysis can be written as polynomial optimization problems with nonconvex sum-of-squares problems. To solve those problems efficiently, we propose a sequential approach of local linearizations leading to tractable, convex sum-of-squares problems. Local convergence is proven under the assumption of strong regularity and the new approach is applied to estimate the region of attraction of a polynomial aircraft model.

Has companion code repository: https://github.com/tcunis/bisosprob

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