FlashFolio: A GPU-Accelerated Solver for Portfolio Optimization
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917433578618880 |
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| author | Jiang, Yilun Lu, Haihao Peng, Zedong Yang, Jinwen |
| author_facet | Jiang, Yilun Lu, Haihao Peng, Zedong Yang, Jinwen |
| contents | We present FlashFolio, a GPU-accelerated solver for single-period and multi-period portfolio optimization with factor-based risk modeling, bid-offer spread costs, and nonlinear market impact. These models are widely used in portfolio construction and optimal execution, but become computationally challenging at large scale, especially in the multi-period setting. We benchmark FlashFolio against MOSEK on instances constructed from realistic market inputs. FlashFolio delivers consistent runtime improvements, achieving speedups of up to 12.9x in the single-period setting and 48x in the multi-period setting, while also exhibiting stronger robustness on challenging multi-period instances. Our results show that GPU-based optimization can help improve the practicality of large-scale portfolio optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22625 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FlashFolio: A GPU-Accelerated Solver for Portfolio Optimization Jiang, Yilun Lu, Haihao Peng, Zedong Yang, Jinwen Optimization and Control Computational Engineering, Finance, and Science We present FlashFolio, a GPU-accelerated solver for single-period and multi-period portfolio optimization with factor-based risk modeling, bid-offer spread costs, and nonlinear market impact. These models are widely used in portfolio construction and optimal execution, but become computationally challenging at large scale, especially in the multi-period setting. We benchmark FlashFolio against MOSEK on instances constructed from realistic market inputs. FlashFolio delivers consistent runtime improvements, achieving speedups of up to 12.9x in the single-period setting and 48x in the multi-period setting, while also exhibiting stronger robustness on challenging multi-period instances. Our results show that GPU-based optimization can help improve the practicality of large-scale portfolio optimization. |
| title | FlashFolio: A GPU-Accelerated Solver for Portfolio Optimization |
| topic | Optimization and Control Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2604.22625 |