The Efficient Frontier from a LASSO Solver: A practical guide to the CLA–LASSO identity

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Highly relevant. Provides a significantly faster and more accurate method for computing the efficient frontier compared to traditional quadratic programming grids. Enables real-time or near-real-time portfolio rebalancing and risk analysis for large portfolios (e.g., S&P 500).

Moderate. Requires understanding of the mathematical transformation from mean-variance to least squares and the specific 'shift' trick for the long-only case. However, the actual coding leverages existing libraries (scikit-learn, numpy), making it accessible to practitioners familiar with these tools.

High reproducibility. The paper provides a GitHub repository with Python scripts (make_figures.py) that regenerate all figures and numbers. It includes pinned dependencies and tests. Simulated data uses fixed seeds. Real data fetching script is provided.