Infeasibility as Diagnostic: IIS Decomposition for Auditing Business Rules in Portfolio Optimization
Infeasibility is conventionally treated as a solver failure. We argue the opposite: an infeasible model is the most valuable diagnostic an optimization system can produce. Using Irreducible Infeasible Subsystem (IIS) decomposition combined with shadow-price analysis, we show how conflict detection surfaces business-rule contradictions — ESG mandates against minimum-yield floors, sector caps against liquidity requirements — that humans cannot reliably enumerate. We present a case study on a $240M multi-asset portfolio where IIS resolution exposed a $2.1M latent tradeoff and triggered a documented, auditable human override.