IndisputableMonolith.Mathematics.OptimizationProblemClassesFromConfigDim
This module defines classes of optimization problems derived from configuration dimension within the Recognition Science mathematics layer. Researchers classifying discrete problems in RS-native units would reference these classes when setting up dimensional parameters. The module consists entirely of definitions and certificates with no theorems or proofs.
claimLet $O$ be the type of optimization classes derived from configuration dimension. Let $c: O → ℕ$ be the count function and let $C$ be the certification structure for the classes.
background
The module sits in the Mathematics domain and imports only Mathlib together with IndisputableMonolith.Constants. The sole upstream dependency supplies the RS time quantum τ₀ = 1 tick. It introduces the sibling definitions OptimizationClass (the classification type), optimizationClass_count (the counting map), OptimizationClassesCert and optimizationClassesCert (the certification objects).
proof idea
This is a definition module, no proofs.
why it matters in Recognition Science
The module supplies the classification objects required by any later theorems that classify optimization problems by configuration dimension. It therefore supports the mathematical infrastructure underlying the forcing chain T0–T8 and the Recognition Composition Law, even though no direct used_by edges are recorded.
scope and limits
- Does not contain theorem statements or proofs.
- Does not import any physics-specific modules.
- Does not assign numerical values to constants.
- Does not depend on the forcing chain or RCL lemmas.