# An Efficient Solution Method for Solving Convex Separable Quadratic Optimization Problems

arXiv: https://arxiv.org/abs/2510.11554 · HTML: https://mlsanity.com/paper/2510.11554/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2025.

## Built on by (most cited first, 2 of 2)

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- [Boosted Distributional Reinforcement Learning: Analysis and Healthcare Applications](https://mlsanity.com/paper/2604.04334/) (2026)

## Abstract (arXiv)

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.