Analysis of Sequential Quadratic Programming through the Lens of Riemannian Optimization

arXiv: Optimization and Control, 2019.

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Abstract:

We prove that a first-order Sequential Quadratic Programming (SQP) algorithm for equality constrained optimization has local linear convergence with rate $(1-1/kappa_R)^k$, where $kappa_R$ is the condition number of the Riemannian Hessian, and global convergence with rate $k^{-1/4}$. Our analysis builds on insights from Riemannian optimiz...More

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