Papers › Sequential sum-of-squares programming for analysis of nonlinear systems
Sequential sum-of-squares programming for analysis of nonlinear systems
Torbjørn Cunis, Benoît Legat
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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.
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