# Analysis of Sequential Quadratic Programming through the Lens of Riemannian Optimization

arXiv: https://arxiv.org/abs/1805.08756 · HTML: https://mlsanity.com/paper/1805.08756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

## Built on by (most cited first, 1 of 1)

- [Elastica Models for Color Image Regularization](https://mlsanity.com/paper/2203.09995/) (2022)

## Abstract (arXiv)

We prove that a "first-order" Sequential Quadratic Programming (SQP) algorithm for equality constrained optimization has local linear convergence with rate (1-1/kappaR)k, where kappaR is the condition number of the Riemannian Hessian, and global convergence with rate k-1/4. Our analysis builds on insights from Riemannian optimization -- we show that the SQP and Riemannian gradient methods have nearly identical behavior near the constraint manifold, which could be of broader interest for understanding constrained optimization.