# Autonomous Sparse Mean-CVaR Portfolio Optimization

arXiv: https://arxiv.org/abs/2405.08047 · HTML: https://mlsanity.com/paper/2405.08047/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 2 of 2)

- [A Scalable Algorithm For Sparse Portfolio Selection](https://mlsanity.com/paper/1811.00138/) (2018)
- [Bilevel Cutting-plane Algorithm for Solving Cardinality-constrained Mean-CVaR Portfolio Optimization Problems](https://mlsanity.com/paper/2005.12797/) (2020)

## Built on by (most cited first, 1 of 1)

- [Linear Trading Position with Sparse Spectrum](https://mlsanity.com/paper/2508.18596/) (2025)

## Abstract (arXiv)

The ℓ0-constrained mean-CVaR model poses a significant challenge due to its NP-hard nature, typically tackled through combinatorial methods characterized by high computational demands. From a markedly different perspective, we propose an innovative autonomous sparse mean-CVaR portfolio model, capable of approximating the original ℓ0-constrained mean-CVaR model with arbitrary accuracy. The core idea is to convert the ℓ0 constraint into an indicator function and subsequently handle it through a tailed approximation. We then propose a proximal alternating linearized minimization algorithm, coupled with a nested fixed-point proximity algorithm (both convergent), to iteratively solve the model. Autonomy in sparsity refers to retaining a significant portion of assets within the selected asset pool during adjustments in pool size. Consequently, our framework offers a theoretically guaranteed approximation of the ℓ0-constrained mean-CVaR model, improving computational efficiency while providing a robust asset selection scheme.