Khalim Conn-Kowlessar 05a4f5f84a feat(modelling): optimise_min_cost — least-cost-to-target selector (#1152 follow-up) ... Exact-enumeration sibling to optimise(): pick <=1 option per group to minimise total cost subject to total gain >= target_gain and cost <= budget (None = unconstrained). Ties broken toward higher gain ('recommend more'). Returns None when no package within budget reaches the target (caller falls back to max-gain); a non-positive target is met by the empty package. This is the warm-start objective for an Increasing EPC goal per the ADR-0016 amendment (least-cost-to-target, not max-gain). Dependency-blind for now; ventilation-aware selection lands in a later slice. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>		2026-06-03 15:31:26 +00:00
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__init__.py	refactor(modelling): group domain/modelling into generators/scoring/optimisation	2026-06-03 13:48:36 +00:00
measure_dependency.py	refactor(modelling): ventilation_dependency delegates to the generator + wraps	2026-06-03 14:04:17 +00:00
optimiser.py	feat(modelling): optimise_min_cost — least-cost-to-target selector (#1152 follow-up)	2026-06-03 15:31:26 +00:00