Exhaustive binary search

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

Enumerates every size-K subset and returns the one with the smallest full mean–variance objective.

Dependencies: NumPy; no extra dependencies.

Key parameters: k fixes the subset size; alpha is accepted for objective scoring and vanishes for feasible selections.

Guarantees and limits: Finds a global binary optimum by enumeration, subject to floating-point scoring. Work grows with the binomial coefficient, so this is a small-instance reference.

Example

Use the shared inputs.

from vqe_portfolio.baselines import exhaustive_binary_baseline

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