Parallel Newton methods for the continuous quadratic knapsack problem: A Jacobi and Gauss-Seidel tale

TL;DR

This paper enhances the semismooth Newton method for the continuous quadratic knapsack problem, enabling efficient parallel algorithms for CPU and GPU, with implementation in an open-source Julia package and extensive performance testing.

Contribution

It introduces improved parallel Newton methods for CQK, leveraging Condat's initial guess and flexible algorithm design, implemented in Julia for better scalability.

Findings

The new methods outperform existing solvers in efficiency.

Parallel variants show significant scalability on CPU and GPU.

Open-source Julia package facilitates adoption and testing.

Abstract

The continuous quadratic knapsack (CQK) problem involves minimizing a diagonal convex quadratic function subject to box constraints and a single linear equality constraint. It has numerous applications in resource allocation, multicommodity flow, machine learning, and classical optimization tasks such as Lagrangian relaxation and quasi-Newton updates. In this work, we revisit the semismooth Newton method introduced by Cominetti, Mascarenhas, and Silva. We demonstrate that the method can be significantly improved in two directions. First, for projections onto the simplex or the $\ell_1$-ball, it can incorporate Condat's highly effective initial multiplier guess. Second, it can serve as a flexible foundation for CQK algorithms, allowing for different parallel variants tailored to exploit CPU and GPU computational models. These improvements are implemented in the open-source Julia package \texttt{NewtonCQK.jl}. We present extensive numerical tests comparing this implementation with other state-of-the-art solvers, demonstrating its superior efficiency and scalability.