AI & ML
Vector Lyapunov functions for nonconvex constrained optimization
arXiv cs.SY0 views
arXiv:2611.08729v1 Announce Type: cross Abstract: Nonconvex constrained optimization problems are commonly solved by a Newton method for necessary conditions (sequential linear/quadratic programming) or a barrier reformulation (interior-point methods). We present a vector Lyapunov analysis for Newton-type algorithms based on constraint violation and optimality of the primal solutions to a sequence of approximate optimization problems. We thus prove local stability and convergence by constructing a strict Lyapunov function for the primal solution.
Source：https://arxiv.org/abs/2611.08729PreviousSpiking neuromodulation: a linear control perspectiveNextFishSense Lite: A Camera-Based, Single Laser Hardware Framework with Software System for In Situ Fish Length Measurement
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