strategy='auto' now routes every scenario-mode CVaR optimisation through
quantcore.optimization.large_scale (LP relaxation on the tail rows, screening,
round-and-repair, certified against the relaxation bound, escalation to the
integer MILP otherwise); strategy='milp' is the explicit opt-out, and
row_generation applies to that path only. When the escalated MILP proves the
rounded portfolio optimal within its gap, the result is Optimal even where the
relaxation bound is loose (small portfolios with a real integrality gap).
UI: a Strategy selector and the driver's bound, gap, screened size, rows and
solver calls in the provenance card. Deploy env examples document the new
cuOpt settings (presolve off, dual simplex for LPs, 50 ms polling). The
service-equivalence test asks for strategy='milp' where it checks the
integer row-generation path.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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