5 Convex optimization
5.1 Introduction
This is how we minimize \(\sigma\).
- Linear programming
- George Dantzig (1914-2005)
- Quadratic programming
- No-shorts efficient frontier
- Karush-Kuhn-Tucker (KKT) conditions
- Jagannathan, R. & Ma, T. (2003). Risk reduction in large portfolios: Why imposing the wrong constraints helps. 1
- Tam, A.S. (2021). Lagrangians and portfolio optimization.
- Boyd, S. & Vandenberghe, L. (2004). Convex Optimization. 2
- Markowitz’s Critical Line Algorithm (CLA)
- Markowitz, H.M. (1956). The optimization of a quadratic function subject to linear constraints. 3
- Bailey, D.H. & López de Prado, M. (2013). An open-source implementation of the critical-line algorithm for portfolio optimization. 4
- Markowitz, H.M., Starer, D., Fram, H., & Gerber, S. (2019). Avoiding the downside: A practical review of the Critical Line Algorithm for mean-semivariance portfolio optimization. 5
- Software:
1 Jagannathan & Ma (2003).
2 Boyd & Vandenberghe (2004).
3 Markowitz (1956).
4 Bailey & López de Prado (2013).
5 Markowitz, Starer, Fram, & Gerber (2019).
TODO: Discuss optimizing the no-shorts frontier.