Infinite-dimensional operator ridgelet transform

 Infinite-dimensional operator ridgelet transform🔗

This Blueprint follows the manuscript on the Gaussian-weighted ridgelet transform on an infinite-dimensional Hilbert space section by section: networks with Hilbert-space inputs, the transform and its Plancherel theory, representation and reconstruction, tempered synthesis activations, finite-width approximation, and the examples with genuinely infinite-dimensional inputs, each chapter folding in the appendix that carries its proofs. Every theorem, proposition, lemma, corollary, example, and definition of the manuscript is one node, labeled by its manuscript label (thm:B, lem:fourier-slice, def:ridgelet-analysis, ...) and linked to all of its Lean declarations; the manuscript's examples appear as propositions, since the Blueprint has no example kind. Objects that the manuscript introduces inside prose have auxiliary definition nodes (aux:...), and the infrastructure chapter records the tools that Mathlib lacks. A node has one of three states: a node with a Lean association and a complete proof is done; a node with a Lean association whose proof is still sorry is stated but not proved; a node without a Lean association is a roadmap item. None of the three is an assumption in the formal development, and the status is read from the Lean code, never written by hand.

The record of what is verified is the comparator scheme of the OperatorRidgelet project. Each manuscript item is a theorem OperatorRidgelet.Paper.<kind>_<label>[_<part>], stated twice with identical text, once with proof sorry in the Challenge library and once with the real proof in OperatorRidgelet.Paper; the definitions it uses live in sorry-free definition modules. A statement is verified once it is listed in comparator/config.json and comparator confirms that the proof matches the challenge statement and uses only the axioms propext, Quot.sound, and Classical.choice. The Blueprint reads the same declarations, so a node whose parts are all verified shows as complete, and a multi-part node shows as incomplete while any part is still sorry. Dependencies follow the manuscript's proofs; the graph and the summary below are computed from them. The last chapter is the comparator review, generated from the same data: for every manuscript item it shows the Challenge statement verbatim next to a link to its node and the comparator status of each declaration, so that the formal statement can be checked against the informal one without leaving the site.

Contents

  1. 1. Neural networks with Hilbert-space inputs
  2. 2. The Gaussian-weighted ridgelet transform
  3. 3. Representation and reconstruction
  4. 4. Tempered synthesis activations and ReLU
  5. 5. Approximation by finite-width networks
  6. 6. Examples with genuinely infinite-dimensional inputs
  7. 7. Infrastructure and roadmap
  8. 8. Comparator review of the Challenge statements
  9. Dependency Graph
  10. Blueprint Summary