IndisputableMonolith.Mathematics.OptimizationProblemClassesFromConfigDim
The module defines classes of optimization problems indexed by configuration dimension in the Recognition Science setting. Mathematicians formalizing discrete problem structures in RS would cite these definitions when classifying instances by config dim. It is a definition module that introduces the main types and functions without proofs or theorems.
claimDefines the type $\text{OptimizationClass}$ of optimization problem classes derived from configuration dimension, the counting function $\text{optimizationClass_count}$, and the certification $\text{OptimizationClassesCert}$.
background
The module resides in the Mathematics domain and imports only the RS time quantum $\tau_0 = 1$ tick from Constants. It introduces definitions for optimization problem classes classified by the dimension of the configuration space, using the sibling objects OptimizationClass, optimizationClass_count, OptimizationClassesCert, and optimizationClassesCert. The local setting is the discrete mathematical layer that precedes physical applications of the phi-ladder and forcing chain.
proof idea
this is a definition module, no proofs
why it matters in Recognition Science
Supplies the classification objects that later stages of the Recognition Science monolith apply when mapping problems onto the phi-ladder and eight-tick octave. It feeds the broader framework by providing the mathematical vocabulary for config-dim-indexed optimization classes that support downstream derivations of mass formulas and Berry thresholds.
scope and limits
- Does not contain any theorem statements or Lean proofs.
- Does not import physics modules beyond the constants file.
- Does not define or reference the J-function or Recognition Composition Law.
- Limits scope to type definitions and counting functions for problem classes.