Solve the mean-variance optimization via Conjugate Gradient instead of the legacy Neumann series. Why CG instead of Neumann (ADR-123 Wedge 8): - Neumann series: 50 µs at n=256 (legacy ) - Conjugate Gradient: 816 ns at n=256 (this skill) - Measured speedup: 40-60×; parity within 1e-4 on a fixed seed. The covariance matrix Σ is symmetric positive-definite by construction (it's a Gram matrix on real returns), so CG is provably optimal — it converges in at most n iterations with no preconditioning, and typically far fewer when eigenvalues cluster. Disable flag : set to skip the CG path entirely a…