September 23, 2025MathematicsOpen Access

An Improved Frank–Wolfe Algorithm to Solve the Tactical Investment Portfolio Optimization Problem

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The Frank-Wolfe algorithm produced portfolio weights closely matching the CVXPY-OSQP solver's at lower target returns, yet diverged at higher returns.
A key advantage of the FW method was its shorter execution times and lower memory consumption compared to traditional quadratic programming solutions.
The study reveals a significant trade-off between computational efficiency and accuracy in portfolio optimization methods, indicating areas for further improvement.
Adaptive iteration strategies are highlighted as potential avenues for enhancing FW's performance under challenging optimization scenarios.

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Implication

Comparative analysis reveals FW algorithm efficiently matches CVXPY-OSQP solver in portfolio optimization, suggesting accuracy and efficiency trade-offs.

Cite This Study

Setyawan et al. (2025) studied this question.

synapsesocial.com/papers/68d4724731b076d99fa6aa20 https://doi.org/10.3390/math13183038

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Implication

Comparative analysis reveals FW algorithm efficiently matches CVXPY-OSQP solver in portfolio optimization, suggesting accuracy and efficiency trade-offs.

An Improved Frank–Wolfe Algorithm to Solve the Tactical Investment Portfolio Optimization Problem

Key Points

Discussion

Implication

Cite This Study

An Improved Frank–Wolfe Algorithm to Solve the Tactical Investment Portfolio Optimization Problem

Key Points

Discussion

Implication

Cite This Study

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Also Consider