FlashFolio: A GPU-Accelerated Solver for Portfolio Optimization

2026-04-24

AI Summary

This study addresses the severe computational challenges in large-scale single- and multi-period portfolio optimization when incorporating factor risk models, bid–ask spreads, and nonlinear market impact, particularly the inefficiency and instability of solving multi-period problems. The work presents the first efficient application of GPU parallel computing to multi-period portfolio optimization with nonlinear market impact, integrating customized numerical algorithms to construct a quadratic and nonlinear programming solver capable of handling complex real-world constraints. Compared to the commercial MOSEK solver, the proposed method achieves speedups of up to 12.9× in single-period settings and 48× in multi-period scenarios, while substantially improving solution robustness and accuracy on difficult problem instances.

Technology Category

• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: The sharing economy
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• portfolio optimization
• factor-based risk modeling
• market impact
• multi-period optimization
• computational scalability

Innovation

• GPU acceleration
• portfolio optimization
• multi-period optimization
• factor-based risk modeling
• nonlinear market impact