An Efficient Solution Method for Solving Convex Separable Quadratic Optimization Problems

2025/10/13 by Shaoze Li, Li, Shaoze, Junhao Wu +7
Computer Science · Mathematics · #Advanced Optimization Algorithms Research #FOS: Mathematics #Optimization and Control (math.OC) #Optimization and Variational Analysis #Stochastic Gradient Optimization Techniques

paper · pdf · doi:10.48550/arxiv.2510.11554

openalex publication_date 2025/10/13 · openalex created_date 2025/10/15 · openalex updated_date 2026/07/28

Abstract

Convex separable quadratic optimization problems occur in many practical applications. In this paper, based on an iterative resolution scheme of the KKT system, we develop an efficient method for solving a quadratic programming problem with a convex separable objective function subject to multiple convex separable constraints. We show that the proposed approach leads to a dual coordinate ascent algorithm and provide a convergence proof. Numerical experiments support the superior performance of the proposed method to that of the Gurobi solver, especially for solving large-scale convex separate quadratic programming problems.

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