QPPAL — a MATLAB software for high-dimensional convex quadratic programming

Ling Liang, Xudong Li, Defeng Sun, Kim-Chuan Toh

This software is designed to solve primal convex quadratic programming problems of the form:

\min&\frac{1}{2}\langle x ,\, {Q}x \rangle + \langle c, \, x\rangle
{\rm s.t.}&{A}x=b
&  x\in \mathcal{K}

where \(Q: \mathbb{R}^n \rightarrow \mathbb{R}^n\) is a self-adjoint positive semidefinite linear operator, \(A  \in \mathbb{R}^{n\times m}\) , \(c\in \mathbb{R}^n\), \(b\in \mathbb{R}^{m}\) are given data, \(\mathcal{K}\) is a simple closed convex polyhedral set defined by  \(\mathcal{K}=\{x\in\mathbb{R}^n \mid \ell\leq x\leq u\}\) with \(\ell,u\in\mathbb{R}^n\) being given bounds.

Important note.

  • The software is still under development. Thus it will invariably be buggy. We would appreciate your feedback and bugs’ report.
  • This is a research software. It is not intended nor designed to be a general purpose software at the moment.

  • Ling Liang, Xudong Li, Defeng Sun, and Kim-Chuan Toh, QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems,

Copyright. This version of QPPAL is distributed under the 3-Clause BSD license.
For commercial applications that may be incompatible with this license, please contact the authors to discuss alternatives.

  • Download here: QPPAL.zip
    • unzip the package
    • Run Matlab in the directory QPPAL
    • After that, to see whether you have installed the software correctly, type
      >> runDemo
    • By now, the software is ready for you to use.