Enhancing Quadratic Programming Solvers via Quadratic Nonconvex Reformulation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914011148189696 |
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| author | Lu, Cheng Fei, Yu Kang, Gaojian Qu, Guangai Deng, Zhibin Jin, Qingwei Fang, Shu-Cherng |
| author_facet | Lu, Cheng Fei, Yu Kang, Gaojian Qu, Guangai Deng, Zhibin Jin, Qingwei Fang, Shu-Cherng |
| contents | In this paper, we consider solving nonconvex quadratic programming problems using modern solvers such as Gurobi and SCIP. It is well-known that the classical techniques of quadratic convex reformulation can improve the computational efficiency of global solvers for mixed-integer quadratic optimization problems. In contrast, the use of quadratic nonconvex reformulation (QNR) has not been previously explored. This paper introduces a QNR framework--an unconventional yet highly effective approach for improving the performance of state-of-the-art quadratic programming solvers such as Gurobi and SCIP. Our computational experiments on diverse nonconvex quadratic programming problem instances demonstrate that QNR can substantially accelerate both Gurobi and SCIP. Notably, with QNR, Gurobi achieves state-of-the-art performance on several benchmark and randomly generated instances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Quadratic Programming Solvers via Quadratic Nonconvex Reformulation Lu, Cheng Fei, Yu Kang, Gaojian Qu, Guangai Deng, Zhibin Jin, Qingwei Fang, Shu-Cherng Optimization and Control In this paper, we consider solving nonconvex quadratic programming problems using modern solvers such as Gurobi and SCIP. It is well-known that the classical techniques of quadratic convex reformulation can improve the computational efficiency of global solvers for mixed-integer quadratic optimization problems. In contrast, the use of quadratic nonconvex reformulation (QNR) has not been previously explored. This paper introduces a QNR framework--an unconventional yet highly effective approach for improving the performance of state-of-the-art quadratic programming solvers such as Gurobi and SCIP. Our computational experiments on diverse nonconvex quadratic programming problem instances demonstrate that QNR can substantially accelerate both Gurobi and SCIP. Notably, with QNR, Gurobi achieves state-of-the-art performance on several benchmark and randomly generated instances. |
| title | Enhancing Quadratic Programming Solvers via Quadratic Nonconvex Reformulation |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2508.20897 |