{"ID":2881564,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.11869","arxiv_id":"2508.11869","title":"Solving Quadratic Programs via Deep Unrolled Douglas-Rachford Splitting","abstract":"Convex quadratic programs (QPs) are fundamental to numerous applications, including finance, engineering, and energy systems. Among the various methods for solving them, the Douglas-Rachford (DR) splitting algorithm is notable for its robust convergence properties. Concurrently, the emerging field of Learning-to-Optimize offers promising avenues for enhancing algorithmic performance, with algorithm unrolling receiving considerable attention due to its computational efficiency and interpretability. In this work, we propose an approach that unrolls a modified DR splitting algorithm to efficiently learn solutions for convex QPs. Specifically, we introduce a tailored DR splitting algorithm that replaces the computationally expensive linear system-solving step with a simplified gradient-based update, while retaining convergence guarantees. Consequently, we unroll the resulting DR splitting method and present a well-crafted neural network architecture to predict QP solutions. Our method achieves up to 50% reductions in iteration counts and 40% in solve time across benchmarks on both synthetic and real-world QP datasets, demonstrating its scalability and superior performance in enhancing computational efficiency across varying sizes.","short_abstract":"Convex quadratic programs (QPs) are fundamental to numerous applications, including finance, engineering, and energy systems. Among the various methods for solving them, the Douglas-Rachford (DR) splitting algorithm is notable for its robust convergence properties. Concurrently, the emerging field of Learning-to-Optimi...","url_abs":"https://arxiv.org/abs/2508.11869","url_pdf":"https://arxiv.org/pdf/2508.11869v1","authors":"[\"Jinxin Xiong\",\"Xi Gao\",\"Linxin Yang\",\"Jiang Xue\",\"Xiaodong Luo\",\"Akang Wang\"]","published":"2025-08-16T01:53:15Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}