docs: Doxygen-API + Validierungsprotokoll + Porting-Tutorial

Für einen Mathematiker der unabhängig validieren und eigene Forschung
einbringen möchte.

Doxygen-Kommentare (code/include/):
  newton_solver.hpp — newton_euclidean(), newton_spherical(), newton_hyper_ideal()
    je mit \param, \return, \note, \see inkl. mathematischer Begründung
    (Konvexität, Vorzeichenkonvention, SparseQR-Fallback-Erklärung)
  layout.hpp — euclidean_layout(), spherical_layout(), hyper_ideal_layout()
    mit vollständiger Parameter-Doku, halfedge_uv-Semantik, Poincaré-Disk-Note

Neues Dokument:
  doc/math/validation-protocol.md
    7 reproduzierbare Checks mit konkreten Befehlen und erwartetem Output:
    0. 170 Tests, 1 Skip
    1. Gauss–Bonnet exakt (1e-10)
    2. FD-Gradientencheck < 1e-6 für alle 3 Geometrien
    3. Newton-Konvergenz < 50 Iterationen
    4. τ ∈ SL(2,ℤ)-Fundamentaldomäne (3 Invarianten)
    5. Möbius-Arithmetik (Inverse, Compose, from_three)
    6. End-to-End-Pipeline
    7. Manueller τ-Check für torus_4x4.off (Codebeispiel)

Neues Tutorial:
  doc/tutorials/add-inversive-distance.md
    Vollständiger Step-by-Step-Port von Phase 9a (Luo 2004):
    Header anlegen, Energie/Gradient implementieren, FD-Check,
    Newton-Wrapper, CMakeLists, Java-Referenzvergleich, Checkliste.

doc/getting-started.md:
  Abschnitt "Known issues": macOS-Finder-Duplikate (rm-Befehl),
  Warnung "First build 30–90s" (Tarball-Extraktion)

README.md:
  Zwei neue Links in der Dokumentationstabelle (validation-protocol,
  tutorial)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

code/include/newton_solver.hpp

@@ -139,9 +139,29 @@ inline std::vector<double> line_search(
 } // namespace detail
 
 // ── Euclidean Newton solver ────────────────────────────────────────────────────
-//
-// Minimises the Euclidean discrete conformal energy by solving G(x) = 0.
-// The Hessian H is PSD; Eigen::SimplicialLDLT is used directly.
+
+/// Solve the Euclidean discrete conformal problem: find u ∈ ℝ^V such that
+/// Σ_{faces adj v} α_v(u) = Θ_v for all vertices v.
+///
+/// Starting from x0, Newton's method minimises E(u) (the Euclidean DCE energy,
+/// which is convex) by iterating  u ← u − H⁻¹·G  with backtracking line search.
+/// The Hessian H is the cotangent Laplacian — PSD with one zero eigenvalue on
+/// closed surfaces (gauge mode).  A SparseQR fallback handles this automatically.
+///
+/// \param mesh      Input triangulated surface (edges must carry lambda0 + theta_v).
+/// \param x0        Initial DOF vector (length = number of free vertices).
+///                  Pass all-zeros for a flat start (typical).
+/// \param m         EuclideanMaps: lambda0[e], theta_v[v], v_idx[v] must be set.
+///                  Call setup_euclidean_maps() + compute_euclidean_lambda0_from_mesh()
+///                  + enforce_gauss_bonnet() before passing here.
+/// \param tol       Convergence threshold on max |G_i|.  Default: 1e-8.
+/// \param max_iter  Maximum Newton iterations.  Default: 200.
+/// \return          NewtonResult{x*, iterations, grad_inf_norm, converged}.
+///
+/// \note On closed meshes without a pinned vertex, SimplicialLDLT detects the
+///       gauge singularity and falls back to SparseQR automatically.
+///
+/// \see doc/math/discrete-conformal-theory.md §3 for the mathematical background.
 inline NewtonResult newton_euclidean(
     ConformalMesh&             mesh,
     std::vector<double>        x0,
@@ -199,10 +219,28 @@ inline NewtonResult newton_euclidean(
 }
 
 // ── Spherical Newton solver ───────────────────────────────────────────────────
-//
-// Solves G(x) = 0 for the spherical discrete conformal functional.
-// The Hessian H is NSD at the solution; −H is PSD, so we factorise −H and
-// solve (−H)·Δx = G  ⟺  H·Δx = −G.
+
+/// Solve the spherical discrete conformal problem: find u ∈ ℝ^V such that
+/// Σ_{faces adj v} α_v(u) = Θ_v for all vertices v (genus-0 / sphere-like surfaces).
+///
+/// The spherical DCE energy is *concave*, so the Hessian H is NSD at the solution.
+/// The solver factorises −H (which is PSD) and solves (−H)·Δx = G.
+/// A gauge vertex must be pinned (set v_idx = -1) to remove the rotational mode.
+///
+/// \param mesh      Input triangulated surface, genus 0.
+/// \param x0        Initial DOF vector (length = free vertices, excluding gauge_vertex).
+///                  All-zeros is a good start.
+/// \param m         SphericalMaps: lambda0[e], theta_v[v], v_idx[v], gauge_vertex set.
+///                  Call setup_spherical_maps() + compute_spherical_lambda0_from_mesh()
+///                  + enforce_gauss_bonnet() (checks Σ(2π-Θ) > 0) before passing here.
+/// \param tol       Convergence threshold on max |G_i|.  Default: 1e-8.
+/// \param max_iter  Maximum Newton iterations.  Default: 200.
+/// \return          NewtonResult{x*, iterations, grad_inf_norm, converged}.
+///
+/// \note Unlike the Euclidean solver, the spherical solver does NOT need a SparseQR
+///       fallback — the gauge vertex pins the null mode directly.
+///
+/// \see doc/math/geometry-modes.md §Spherical for sign-convention details.
 inline NewtonResult newton_spherical(
     ConformalMesh&             mesh,
     std::vector<double>        x0,
@@ -258,17 +296,31 @@ inline NewtonResult newton_spherical(
 }
 
 // ── HyperIdeal Newton solver ──────────────────────────────────────────────────
-//
-// Solves G(x) = 0 for the hyper-ideal discrete conformal functional.
-//
-// Gradient sign convention (opposite to Euclidean/Spherical):
-//   G_v = Σ β_v − Θ_v,   G_e = Σ α_e − θ_e   (actual − target)
-//
-// The hyper-ideal energy is strictly convex (Springborn 2020), so H is PSD
-// and SimplicialLDLT (with SparseQR fallback) applies directly.
-//
-// The Hessian is computed by symmetric finite differences of G (see
-// hyper_ideal_hessian.hpp); replace with an analytical Hessian in Phase 5.
+
+/// Solve the hyper-ideal discrete conformal problem: find (b, a) ∈ ℝ^{V+E} such that
+/// Σ β_v(b,a) = Θ_v  and  Σ α_e(b,a) = θ_e  for all vertices v and edges e.
+///
+/// Used for genus-g surfaces (g ≥ 1) under hyperbolic cone metrics.
+/// The energy is *strictly convex* (Springborn 2020, Theorem 1.3), so Newton
+/// converges globally from any starting point.
+///
+/// DOF layout: first V_free entries are vertex variables b_v (hyper-ideal radii),
+/// followed by E entries for edge variables a_e (intersection angles).
+/// Use assign_all_dof_indices(mesh, maps) to set v_idx and e_idx automatically —
+/// no vertex needs to be pinned.
+///
+/// \param mesh      Input triangulated surface, genus g ≥ 1.
+/// \param x0        Initial DOF vector (length = V + E).  All-zeros typical.
+/// \param m         HyperIdealMaps: lambda0[e], theta_v[v], v_idx[v], e_idx[e] set.
+///                  Call setup_hyper_ideal_maps() + compute_hyper_ideal_lambda0_from_mesh().
+/// \param tol       Convergence threshold on max |G_i|.  Default: 1e-8.
+/// \param max_iter  Maximum Newton iterations.  Default: 200.
+/// \param hess_eps  Finite-difference step for Hessian approximation.  Default: 1e-5.
+///                  (Phase 9b will replace this with an analytic Hessian.)
+/// \return          NewtonResult{x*, iterations, grad_inf_norm, converged}.
+///
+/// \see Springborn (2020), Theorem 1.3 for the strict convexity proof.
+/// \see doc/math/geometry-modes.md §Hyper-ideal for DOF layout details.
 inline NewtonResult newton_hyper_ideal(
     ConformalMesh&             mesh,
     std::vector<double>        x0,