Top-return selection

Track: exact-K binary selection. Method ID: top-return.

Selects the K assets with the largest expected returns, then scores the resulting portfolio with the full objective.

Dependencies: NumPy; no extra dependencies.

Key parameters: k controls cardinality, including the empty selection at zero.

Guarantees and limits: Ignores covariance during selection. It is a deterministic comparison baseline, without a general mean–variance optimality guarantee.

Example

Use the shared inputs.

from vqe_portfolio.baselines import top_return_baseline

x, objective = top_return_baseline(mu, Sigma, lam=4.0, alpha=0.0, k=2)