# QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems

arXiv: https://arxiv.org/abs/2103.13108 · HTML: https://mlsanity.com/paper/2103.13108/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [Accelerating Multi-Block Constrained Optimization Through Learning to Optimize](https://mlsanity.com/paper/2409.17320/) (2024)
- [Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm](https://mlsanity.com/paper/2308.08852/) (2023)
- [NewVEM: A Newton Vertex Exchange Method for a Class of Constrained Self-Concordant Minimization Problems](https://mlsanity.com/paper/2407.03294/) (2024)
- [Efficient and provably convergent end-to-end training of deep neural networks with linear constraints](https://mlsanity.com/paper/2605.11526/) (2026)

## Abstract (arXiv)

In this paper, we aim to solve high dimensional convex quadratic programming (QP) problems with a large number of quadratic terms, linear equality and inequality constraints. In order to solve the targeted bf QP problems to a desired accuracy efficiently, we develop a two-phase bf Proximal bf Augmented bf Lagrangian method (QPPAL), with Phase I to generate a reasonably good initial point to warm start Phase II to obtain an accurate solution efficiently. More specifically, in Phase I, based on the recently developed symmetric Gauss-Seidel (sGS) decomposition technique, we design a novel sGS based semi-proximal augmented Lagrangian method for the purpose of finding a solution of low to medium accuracy. Then, in Phase II, a proximal augmented Lagrangian algorithm is proposed to obtain a more accurate solution efficiently. Extensive numerical results evaluating the performance of QPPAL against existing state-of-the-art solvers Gurobi, OSQP and QPALM are presented to demonstrate the high efficiency and robustness of our proposed algorithm for solving various classes of large-scale convex QP problems. The MATLAB implementation of the software package QPPAL is available at: urlhttps://blog.nus.edu.sg/mattohkc/softwares/qppal/.