Binary quadratic optimization (SCIP)

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

Uses binary selection variables, an exact-K constraint, and an unrestricted epigraph variable constrained above the quadratic objective. SCIP solves this formulation without explicit pairwise product variables.

Dependencies: Install vqe-portfolio[scip] or vqe-portfolio[classical] for PySCIPOpt.

Key parameters: time_limit, mip_rel_gap, and an optional candidate_callback control solving and incumbent reporting.

Guarantees and limits: Retains native status, finite dual-bound diagnostics, and gap. Limits can leave a feasible incumbent without optimality. Model construction and cleanup are outside the native time limit; signed couplings and negative objectives are supported.

Example

Use the shared inputs.

from vqe_portfolio.baselines import scip_binary_baseline

result = scip_binary_baseline(mu, Sigma, lam=4.0, k=2, time_limit=10.0)