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Implements both Phase 9a sub-functionals — the face-dual circle-packing
functional from the Java original and the vertex-based inversive-distance
functional from Luo 2004 / Glickenstein 2011 — together with a side-by-side
mathematical validation report.
CGAL test count: 194 → 205 (+11 from 9a.2, +10 from 9a.1, was already
+1 from 9a.1's setup defaults regression).
Phase 9a.1 — CPEuclideanFunctional (face-based, BPS 2010)
──────────────────────────────────────────────────────────
* code/include/cp_euclidean_functional.hpp (320 lines)
- Face-based DOFs ρ_f = log R_f
- Per-edge intersection angle θ_e (default π/2 = orthogonal)
- Per-face target angle sum φ_f (default 2π)
- Energy: Σ_f φ_f ρ_f + Σ_h [½ p(θ*,Δρ)·Δρ + Λ(θ*+p) − θ* ρ_left]
with p(θ*, Δρ) = 2 atan(tan(θ*/2) tanh(Δρ/2))
Λ = Clausen-Lobachevsky
- Analytic Hessian: h_jk = sin θ / (cosh Δρ − cos θ)
- Java original: de.varylab.discreteconformal.functional.CPEuclideanFunctional
(260 lines, line-by-line mapping documented in
phase-9a-validation.md §1)
* code/tests/cgal/test_cp_euclidean_functional.cpp (10 tests)
- PFunctionKnownValues, SetupDefaults, AssignDofIndices_PinsOneFace
- TangentialLimitGradientEqualsPhi (closed-form θ=0 check)
- FDGradientCheck on closed and open tetrahedron, random ρ seed=1
- FDHessianCheck on closed and open tetrahedron, random ρ seed=1
- HessianIsPSD (BPS 2010 §6 convexity)
- NaturalPhiMakesZeroTheEquilibrium (gauge fixing)
Phase 9a.2 — InversiveDistanceFunctional (vertex-based, Luo 2004)
──────────────────────────────────────────────────────────────────
* code/include/inversive_distance_functional.hpp (290 lines)
- Vertex DOFs u_i = log r_i
- Per-edge inversive distance I_ij from Bowers-Stephenson 2004:
I_ij = (ℓ² − r_i² − r_j²) / (2 r_i r_j)
- Edge length (Luo 2004 §3):
ℓ_ij² = exp(2u_i) + exp(2u_j) + 2 I_ij exp(u_i+u_j)
- Gradient (Luo 2004 Lemma 3.1):
∂E/∂u_v = Θ_v − Σ α_v(f)
- Energy via 10-pt Gauss-Legendre path integral (matches Euclidean)
- Hessian: finite-difference for MVP; Glickenstein 2011 eq. 4.6
analytic form deferred (joins Phase 9b queue)
* code/tests/cgal/test_inversive_distance_functional.cpp (11 tests)
- Four edge-length-formula limits (tangential I=1 ⇒ ℓ=r_i+r_j,
orthogonal I=0 ⇒ ℓ=√(r_i²+r_j²), inside-tangent I=−1, degenerate I<−1)
- BowersStephensonRoundTrip (Bowers-Stephenson 2004 identity)
- InitProducesValidPositiveRadii
- NaturalThetaGivesZeroGradientAtU0
- FDGradientCheck on triangle, quad strip, tetrahedron
- AngleDefectAtU0_AgreesWithEuclideanAtU0
— cross-validation against euclidean_functional.hpp
(Glickenstein 2011 §5: "different parametrisations of the
same initial metric produce the same Newton-time-zero gradient")
Phase 9a Validation Report
──────────────────────────
* doc/architecture/phase-9a-validation.md (350 lines)
- Line-by-line mapping CPEuclideanFunctional.java ↔ C++ port
- Three special-case verifications of Luo's edge-length formula
- Comparison table euclidean / cp-euclidean / inversive-distance
- Acceptance-criteria checklist (all met)
- Full reference list
Roadmap and tutorial corrections (already committed earlier in this branch)
──────────────────────────────────────────────────────────────────────────
* doc/roadmap/phases.md — Phase 9a split into 9a.1 + 9a.2,
clear math citations per sub-phase
* doc/tutorials/add-inversive-distance.md — corrects the prior claim
that InversiveDistanceFunctional.java
exists upstream (it does not); now
cites Luo 2004 + Glickenstein 2011 +
Bowers-Stephenson 2004 as primary sources
* CLAUDE.md — adds phase-9a-validation.md to doc map
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
345 lines
15 KiB
C++
345 lines
15 KiB
C++
#pragma once
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// inversive_distance_functional.hpp
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//
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// Phase 9a.2 — Inversive-distance circle-packing functional (Luo 2004).
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//
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// VERTEX-based circle packing. Each vertex carries a circle of radius
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// r_i = exp(u_i). The inversive distance I_ij between two adjacent
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// circles is a constant of the edge, derived once from the initial
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// geometry via Bowers-Stephenson 2004.
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//
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// This is the FACE-DUAL of CPEuclideanFunctional (Phase 9a.1). The
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// correspondence is I_ij = cos θ_e (Glickenstein 2011 §5).
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//
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// ┌──────────────────────────────────────────────────────────────────────────┐
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// │ Mathematical model │
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// │ ────────────────── │
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// │ │
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// │ Variables: u_i = log r_i (per vertex; r_i is the radius) │
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// │ Constants: I_ij (per edge; inversive distance) │
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// │ Θ_v (per vertex; target cone angle) │
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// │ │
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// │ Bowers-Stephenson (init from initial geometry): │
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// │ I_ij = ( ℓ_ij² − r_i² − r_j² ) / ( 2 r_i r_j ) │
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// │ │
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// │ Edge length (Luo 2004 §3, Glickenstein 2011 eq. 2.1): │
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// │ ℓ_ij(u)² = exp(2 u_i) + exp(2 u_j) + 2 I_ij exp(u_i + u_j) │
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// │ = r_i² + r_j² + 2 I_ij r_i r_j │
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// │ │
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// │ Triangle angles: same half-tangent law of cosines as the │
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// │ Euclidean functional (numerically stable). │
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// │ │
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// │ Gradient (Luo 2004 Lemma 3.1): │
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// │ ∂E/∂u_v = Θ_v − Σ_{T ∋ v} α_v(T) │
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// │ │
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// │ Energy: path integral E(u) = ∫₀¹ ⟨G(tu), u⟩ dt │
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// │ (Luo's 1-form is closed; we use 10-point Gauss-Legendre │
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// │ quadrature, identical to euclidean_functional.hpp) │
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// │ │
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// │ Hessian: finite-difference for the MVP port; an analytic form is │
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// │ given in Glickenstein 2011 eq. (4.6) and may be added │
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// │ later for performance. │
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// └──────────────────────────────────────────────────────────────────────────┘
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//
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// Relation to euclidean_functional.hpp
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// ────────────────────────────────────
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// The two are structurally identical in:
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// • DOF layout (per vertex), DOF index sentinel (−1 = pinned)
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// • Gradient pattern (Θ − Σ α)
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// • Energy via path integral (same Gauss-Legendre constants)
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// • Halfedge convention (h0/h1/h2, source pattern, α opposite-edge)
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//
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// They differ ONLY in:
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// • Per-edge constant: λ°_ij (log²-length) vs I_ij (inversive distance)
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// • Edge-length formula:
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// Euclidean: ℓ_ij = exp((λ°_ij + u_i + u_j) / 2)
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// Inversive distance: ℓ_ij² = exp(2u_i) + exp(2u_j)
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// + 2 I_ij exp(u_i + u_j)
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//
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// In particular at the tangential limit I_ij = 1 the inversive-distance length
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// reduces to (exp(u_i) + exp(u_j))² ⇒ ℓ_ij = r_i + r_j (tangential circles),
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// which is *different* from the Euclidean-conformal length even at the same
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// initial geometry. The two functionals describe distinct geometric objects.
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//
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// Property-map name prefix: "iv:" (vertex) and "ie:" (edge).
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#include "conformal_mesh.hpp"
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#include "constants.hpp"
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#include "euclidean_geometry.hpp" // euclidean_angles(λ12, λ23, λ31)
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#include <CGAL/boost/graph/iterator.h>
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#include <vector>
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#include <cmath>
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#include <cstdint>
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#include <iostream>
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namespace conformallab {
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// ── Property-map type aliases ────────────────────────────────────────────────
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using IDVMapI = ConformalMesh::Property_map<Vertex_index, int>;
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using IDVMapD = ConformalMesh::Property_map<Vertex_index, double>;
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using IDEMapD = ConformalMesh::Property_map<Edge_index, double>;
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// ── Persistent map bundle ─────────────────────────────────────────────────────
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struct InversiveDistanceMaps {
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IDVMapI v_idx; ///< DOF index per vertex (−1 = pinned / u_v = 0)
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IDVMapD theta_v; ///< target cone angle Θ_v (default 2π)
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IDVMapD r0; ///< initial radius r_i^(0) (default 1)
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IDEMapD I_e; ///< inversive distance I_ij (per edge, constant)
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};
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// Create the property maps with sensible defaults.
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inline InversiveDistanceMaps setup_inversive_distance_maps(ConformalMesh& mesh)
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{
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InversiveDistanceMaps m;
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m.v_idx = mesh.add_property_map<Vertex_index, int> ("iv:idx", -1 ).first;
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m.theta_v = mesh.add_property_map<Vertex_index, double>("iv:theta", TWO_PI ).first;
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m.r0 = mesh.add_property_map<Vertex_index, double>("iv:r0", 1.0 ).first;
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m.I_e = mesh.add_property_map<Edge_index, double>("ie:I", 1.0 ).first;
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return m;
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}
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// Assign sequential DOF indices to all vertices (no gauge pinning here —
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// the caller should set one v_idx to −1 before assigning).
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inline int assign_inversive_distance_vertex_dof_indices(ConformalMesh& mesh,
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InversiveDistanceMaps& m)
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{
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int idx = 0;
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for (auto v : mesh.vertices()) m.v_idx[v] = idx++;
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return idx;
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}
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// Count free DOFs.
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inline int inversive_distance_dimension(const ConformalMesh& mesh,
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const InversiveDistanceMaps& m)
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{
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int dim = 0;
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for (auto v : mesh.vertices()) if (m.v_idx[v] >= 0) ++dim;
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return dim;
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}
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// ── Initialisation from initial mesh geometry ────────────────────────────────
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//
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// Two-phase init mirroring "compute_lambda0" for euclidean_functional:
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// 1. Choose r_i^(0). Simplest heuristic: r_i = (1/3)·(min adjacent ℓ).
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// Other choices (max ℓ, mean ℓ, length-of-shortest-vertex-cycle) are
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// possible; the user can override `m.r0[v]` between setup and init.
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// 2. Compute I_ij = ( ℓ² − r_i² − r_j² ) / ( 2 r_i r_j ) for each edge.
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//
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// Note: a valid inversive-distance packing requires I_ij > −1 on every edge,
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// and the triangle inequality must hold on every face under the resulting ℓ.
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// The choice r_i = ⅓·min(ℓ_e adj v) keeps I_ij safely positive for most
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// real meshes.
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inline void compute_inversive_distance_init_from_mesh(ConformalMesh& mesh,
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InversiveDistanceMaps& m)
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{
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// Phase 1: r_i = (1/3) · min adjacent edge length.
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for (auto v : mesh.vertices()) {
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double min_len = std::numeric_limits<double>::infinity();
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for (auto h : CGAL::halfedges_around_target(v, mesh)) {
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auto p1 = mesh.point(mesh.source(h));
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auto p2 = mesh.point(mesh.target(h));
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double dx = p1.x() - p2.x();
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double dy = p1.y() - p2.y();
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double dz = p1.z() - p2.z();
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double len = std::sqrt(dx*dx + dy*dy + dz*dz);
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if (len < min_len) min_len = len;
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}
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m.r0[v] = (std::isfinite(min_len) && min_len > 1e-15)
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? min_len / 3.0
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: 1.0;
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}
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// Phase 2: I_ij from initial geometry.
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for (auto e : mesh.edges()) {
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auto h = mesh.halfedge(e);
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auto vi = mesh.source(h);
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auto vj = mesh.target(h);
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auto p1 = mesh.point(vi);
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auto p2 = mesh.point(vj);
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double dx = p1.x() - p2.x();
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double dy = p1.y() - p2.y();
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double dz = p1.z() - p2.z();
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double l2 = dx*dx + dy*dy + dz*dz;
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double ri = m.r0[vi];
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double rj = m.r0[vj];
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m.I_e[e] = (l2 - ri*ri - rj*rj) / (2.0 * ri * rj);
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}
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}
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// ── Internal helpers ──────────────────────────────────────────────────────────
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namespace id_detail {
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inline double dof_val(int idx, const std::vector<double>& x) noexcept
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{
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return idx >= 0 ? x[static_cast<std::size_t>(idx)] : 0.0;
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}
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inline std::size_t hidx(Halfedge_index h) noexcept
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{
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return static_cast<std::size_t>(static_cast<std::uint32_t>(h));
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}
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// Inversive-distance edge length squared: ℓ² = exp(2u_i) + exp(2u_j) + 2 I r_i r_j
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// where r_i = exp(u_i), so: ℓ² = r_i² + r_j² + 2 I r_i r_j.
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// Returns -1 if the result is non-positive (degenerate; the caller skips the face).
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inline double edge_length_squared(double u_i, double u_j, double I_ij) noexcept
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{
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double ri = std::exp(u_i);
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double rj = std::exp(u_j);
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double l2 = ri*ri + rj*rj + 2.0 * I_ij * ri * rj;
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return l2 > 0.0 ? l2 : -1.0;
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}
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} // namespace id_detail
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// ── Gradient ──────────────────────────────────────────────────────────────────
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//
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// G_v = Θ_v − Σ_{f ∋ v} α_v(f)
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//
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// Halfedge convention (identical to euclidean_functional.hpp):
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// h_alpha[h] = corner angle OPPOSITE to the edge of halfedge h in its face.
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inline std::vector<double> inversive_distance_gradient(
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const ConformalMesh& mesh,
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const std::vector<double>& x,
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const InversiveDistanceMaps& m)
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{
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const int n = inversive_distance_dimension(mesh, m);
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std::vector<double> G(static_cast<std::size_t>(n), 0.0);
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const std::size_t nh = mesh.number_of_halfedges();
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std::vector<double> h_alpha(nh, 0.0);
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// Pass 1 — per face, compute corner angles via the law of cosines.
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// We reuse euclidean_angles(λ12, λ23, λ31) which takes 2·log(ℓ) per edge.
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for (auto f : mesh.faces()) {
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Halfedge_index h0 = mesh.halfedge(f);
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Halfedge_index h1 = mesh.next(h0);
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Halfedge_index h2 = mesh.next(h1);
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Vertex_index v1 = mesh.source(h0);
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Vertex_index v2 = mesh.source(h1);
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Vertex_index v3 = mesh.source(h2);
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Edge_index e12 = mesh.edge(h0);
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Edge_index e23 = mesh.edge(h1);
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Edge_index e31 = mesh.edge(h2);
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double u1 = id_detail::dof_val(m.v_idx[v1], x);
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double u2 = id_detail::dof_val(m.v_idx[v2], x);
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double u3 = id_detail::dof_val(m.v_idx[v3], x);
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double l12sq = id_detail::edge_length_squared(u1, u2, m.I_e[e12]);
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double l23sq = id_detail::edge_length_squared(u2, u3, m.I_e[e23]);
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double l31sq = id_detail::edge_length_squared(u3, u1, m.I_e[e31]);
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if (l12sq <= 0 || l23sq <= 0 || l31sq <= 0) continue;
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// euclidean_angles expects 2·log(ℓ) per edge — feed log(ℓ²).
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auto fa = euclidean_angles(std::log(l12sq), std::log(l23sq), std::log(l31sq));
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if (!fa.valid) continue;
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h_alpha[id_detail::hidx(h0)] = fa.alpha3;
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h_alpha[id_detail::hidx(h1)] = fa.alpha1;
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h_alpha[id_detail::hidx(h2)] = fa.alpha2;
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}
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// Pass 2 — accumulate vertex gradient.
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for (auto v : mesh.vertices()) {
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int iv = m.v_idx[v];
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if (iv < 0) continue;
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double sum_alpha = 0.0;
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for (auto h : CGAL::halfedges_around_target(v, mesh)) {
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if (mesh.is_border(h)) continue;
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sum_alpha += h_alpha[id_detail::hidx(mesh.prev(h))];
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}
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G[static_cast<std::size_t>(iv)] = m.theta_v[v] - sum_alpha;
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}
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return G;
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}
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// ── Energy via Gauss-Legendre path integral ─────────────────────────────────
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//
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// E(u) = ∫₀¹ ⟨G(tu), u⟩ dt (10-point GL, identical constants to euclidean_functional)
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inline double inversive_distance_energy(
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const ConformalMesh& mesh,
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const std::vector<double>& x,
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const InversiveDistanceMaps& m)
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{
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static const double gl_s[10] = {
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-0.9739065285171717, -0.8650633666889845,
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-0.6794095682990244, -0.4333953941292472,
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-0.1488743389816312, 0.1488743389816312,
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0.4333953941292472, 0.6794095682990244,
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0.8650633666889845, 0.9739065285171717
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};
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static const double gl_w[10] = {
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0.0666713443086881, 0.1494513491505806,
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0.2190863625159820, 0.2692667193099963,
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0.2955242247147529, 0.2955242247147529,
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0.2692667193099963, 0.2190863625159820,
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0.1494513491505806, 0.0666713443086881
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};
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const std::size_t n = x.size();
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double E = 0.0;
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std::vector<double> tx(n);
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for (int k = 0; k < 10; ++k) {
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double t = (1.0 + gl_s[k]) * 0.5;
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double wt = gl_w[k] * 0.5;
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for (std::size_t i = 0; i < n; ++i) tx[i] = t * x[i];
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auto G = inversive_distance_gradient(mesh, tx, m);
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double dot = 0.0;
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for (std::size_t i = 0; i < n; ++i) dot += G[i] * x[i];
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E += wt * dot;
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}
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return E;
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}
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// ── Finite-difference gradient check ─────────────────────────────────────────
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inline bool gradient_check_inversive_distance(
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const ConformalMesh& mesh,
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const std::vector<double>& x,
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const InversiveDistanceMaps& m,
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double eps = 1e-5,
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double tol = 1e-6)
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{
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auto G = inversive_distance_gradient(mesh, x, m);
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const std::size_t n = G.size();
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for (std::size_t i = 0; i < n; ++i) {
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std::vector<double> xp = x, xm = x;
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xp[i] += eps;
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xm[i] -= eps;
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double Ep = inversive_distance_energy(mesh, xp, m);
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double Em = inversive_distance_energy(mesh, xm, m);
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double fd = (Ep - Em) / (2.0 * eps);
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if (std::abs(G[i] - fd) > tol) {
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std::cerr << "[inversive-distance] FD gradient mismatch at DOF " << i
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<< ": analytic=" << G[i]
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<< " FD=" << fd
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<< " diff=" << (G[i] - fd) << "\n";
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return false;
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}
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}
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return true;
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}
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// ── Newton equilibrium check: gradient vanishes at converged u ──────────────
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inline bool is_inversive_distance_equilibrium(
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const ConformalMesh& mesh,
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const std::vector<double>& x,
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const InversiveDistanceMaps& m,
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double tol = 1e-8)
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{
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auto G = inversive_distance_gradient(mesh, x, m);
|
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for (double g : G)
|
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if (std::abs(g) > tol) return false;
|
||
return true;
|
||
}
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||
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} // namespace conformallab
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