test(euclidean): Tier-1 circular-edge φ cross-validation + cyclic-solve blocker
Build-verified attempt to port the Java EuclideanCyclicConvergenceTest (cathead, "circular hole edge" φ = π−0.1). - ✅ GREEN `CyclicCircularEdge_PhiEntersGradient_CatHead`: solver-free cross-validation that the circular-edge φ target enters the cyclic edge gradient exactly (ΔG_e = −Δφ_e at 1e-12; no other component moves). - ⏸️ DISABLED `CyclicCircularEdge_CatHead_JavaXVal`: the full Java convergence assertion (α_opp+α_opp = π−0.1), kept with golden semantics. Auto-activates once the edge-DOF Hessian lands. Build-verification finding: `newton_euclidean` -> `euclidean_hessian` throws "edge DOFs are not supported" — the cyclic full solve is blocked by a missing edge-DOF Euclidean Hessian (gradient supports edge DOFs, analytic Hessian does not). Spherical convergence (Tier-1 #2) is already covered by test_newton_solver. Documented in doc/reviewer/java-ignore-crossvalidation.md. All 13 EuclideanFunctional tests pass (1 disabled). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Tarik Moussa
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Tarik Moussa
Tarik Moussa
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@@ -27,9 +27,12 @@
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#include "euclidean_functional.hpp"
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#include "euclidean_hessian.hpp"
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#include "clausen.hpp"
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#include "mesh_io.hpp"
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#include "newton_solver.hpp"
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#include <gtest/gtest.h>
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#include <cmath>
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#include <vector>
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#include <string>
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using namespace conformallab;
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@@ -405,3 +408,148 @@ TEST(EuclideanGoldenJava, FullMeshGradientAndEnergy_Tetrahedron)
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- euclidean_energy(mesh, x0, maps);
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EXPECT_NEAR(dE, 0.15962619236187336, 1e-12);
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}
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// ════════════════════════════════════════════════════════════════════════════
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// Java cross-validation (Tier 1, GREEN) — circular-edge φ wiring (no solver)
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//
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// The "circular hole edge" of EuclideanCyclicConvergenceTest works by setting a
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// non-default edge turn angle φ_e. Since the cyclic edge gradient is exactly
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// G_e = α_opp(f⁺) + α_opp(f⁻) − φ_e,
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// lowering φ_e by 0.1 must raise G_e by exactly 0.1 — independent of geometry —
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// and must leave every other gradient component untouched. This pins the φ
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// wiring without needing the (not-yet-implemented) edge-DOF Hessian, so it runs
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// today and is the evaluation-level prerequisite of the DISABLED convergence
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// test below.
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// ════════════════════════════════════════════════════════════════════════════
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TEST(EuclideanFunctional, CyclicCircularEdge_PhiEntersGradient_CatHead)
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{
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const std::string path = std::string(CONFORMALLAB_DATA_DIR) + "/obj/cathead.obj";
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ConformalMesh mesh;
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ASSERT_NO_THROW(mesh = load_mesh(path)) << "cathead.obj not found: " << path;
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auto maps = setup_euclidean_maps(mesh);
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compute_euclidean_lambda0_from_mesh(mesh, maps);
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int idx = 0;
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for (auto v : mesh.vertices())
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maps.v_idx[v] = mesh.is_border(v) ? -1 : idx++;
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for (auto e : mesh.edges())
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maps.e_idx[e] = idx++;
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const int n = idx;
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ASSERT_GT(n, 0);
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Edge_index e_circ{};
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bool found = false;
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for (auto e : mesh.edges()) {
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auto h = mesh.halfedge(e);
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auto ho = mesh.opposite(h);
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if (mesh.is_border(h) || mesh.is_border(ho)) continue;
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e_circ = e; found = true; break;
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}
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ASSERT_TRUE(found) << "no interior edge found on cathead";
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const std::size_t ie = static_cast<std::size_t>(maps.e_idx[e_circ]);
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std::vector<double> x(static_cast<std::size_t>(n), 0.0);
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maps.phi_e[e_circ] = PI;
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auto G1 = euclidean_gradient(mesh, x, maps);
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maps.phi_e[e_circ] = PI - 0.1;
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auto G2 = euclidean_gradient(mesh, x, maps);
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// Lowering φ_e by 0.1 raises exactly this edge's gradient component by 0.1.
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EXPECT_NEAR(0.1, G2[ie] - G1[ie], 1e-12)
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<< "circular-edge φ target not wired into the cyclic gradient";
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// No other gradient component changes.
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double max_other = 0.0;
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for (std::size_t k = 0; k < G1.size(); ++k)
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if (k != ie) max_other = std::max(max_other, std::abs(G2[k] - G1[k]));
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EXPECT_LT(max_other, 1e-12)
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<< "changing one φ_e perturbed unrelated gradient components";
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}
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// ════════════════════════════════════════════════════════════════════════════
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// Java cross-validation (Tier 1) — EuclideanCyclicConvergenceTest
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//
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// Ports de.varylab.discreteconformal.functional.EuclideanCyclicConvergenceTest:
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// prescribe a non-default edge turn angle φ = π − 0.1 on one interior edge of
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// cathead.obj ("circular hole edge"), solve the cyclic Euclidean functional
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// (vertex + edge DOFs), then assert the realised opposite-corner-angle sum
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// across that edge equals π − 0.1.
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//
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// The C++ edge gradient is G_e = α_opp(f⁺) + α_opp(f⁻) − φ_e, so at the
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// solution (G_e = 0) the geometric angle sum equals φ_e — exactly the Java
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// assertion `circularEdge.getAlpha() + opposite.getAlpha() == π − 0.1`.
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//
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// "Natural targets" first make x = 0 the equilibrium (so the *only* deviation
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// is the prescribed φ); the test therefore FAILS if the solver ignores a
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// non-default φ (the sum would stay at its natural value, not π − 0.1).
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//
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// ⚠️ DISABLED (build-verified 2026-05-29): blocked by a missing feature, not a
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// bug. `newton_euclidean` uses `euclidean_hessian`, which throws
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// "euclidean_hessian: edge DOFs are not supported
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// (only the vertex-block cotangent Laplacian is implemented)".
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// The cyclic functional needs vertex+edge DOFs, so the full Newton solve is not
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// yet possible in C++ (the gradient supports edge DOFs; the analytic Hessian
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// does not). PREREQUISITE: edge-DOF Euclidean Hessian — see
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// `doc/roadmap/research-track.md` and `doc/reviewer/java-ignore-crossvalidation.md`.
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// The golden semantics (φ = π−0.1 ⇒ realised α_opp+α_opp = π−0.1) are kept here
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// so this test auto-activates once that Hessian lands; just drop the DISABLED_.
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// ════════════════════════════════════════════════════════════════════════════
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TEST(EuclideanFunctional, DISABLED_CyclicCircularEdge_CatHead_JavaXVal)
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{
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const std::string path = std::string(CONFORMALLAB_DATA_DIR) + "/obj/cathead.obj";
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ConformalMesh mesh;
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ASSERT_NO_THROW(mesh = load_mesh(path)) << "cathead.obj not found: " << path;
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auto maps = setup_euclidean_maps(mesh);
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compute_euclidean_lambda0_from_mesh(mesh, maps);
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// Cyclic DOFs: interior vertices (border pinned) + all edges.
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int idx = 0;
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for (auto v : mesh.vertices())
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maps.v_idx[v] = mesh.is_border(v) ? -1 : idx++;
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for (auto e : mesh.edges())
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maps.e_idx[e] = idx++;
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const int n = idx;
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ASSERT_GT(n, 0);
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// Natural targets: set Θ_v / φ_e so that x = 0 is the equilibrium (G(0)=0).
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std::vector<double> x0(static_cast<std::size_t>(n), 0.0);
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auto G0 = euclidean_gradient(mesh, x0, maps);
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for (auto v : mesh.vertices()) {
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int iv = maps.v_idx[v];
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if (iv >= 0) maps.theta_v[v] -= G0[static_cast<std::size_t>(iv)];
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}
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for (auto e : mesh.edges()) {
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int ie = maps.e_idx[e];
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if (ie >= 0) maps.phi_e[e] += G0[static_cast<std::size_t>(ie)];
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}
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// Pick one interior edge: both incident faces present, both endpoints interior.
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Edge_index circular{};
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bool found = false;
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for (auto e : mesh.edges()) {
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auto h = mesh.halfedge(e);
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auto ho = mesh.opposite(h);
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if (mesh.is_border(h) || mesh.is_border(ho)) continue;
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if (mesh.is_border(mesh.source(h)) || mesh.is_border(mesh.target(h))) continue;
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circular = e; found = true; break;
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}
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ASSERT_TRUE(found) << "no interior edge found on cathead";
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const std::size_t ie = static_cast<std::size_t>(maps.e_idx[circular]);
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// Prescribe the circular edge turn angle φ = π − 0.1 (Java CustomEdgeInfo.phi).
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const double phi_target = PI - 0.1;
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maps.phi_e[circular] = phi_target;
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auto res = newton_euclidean(mesh, x0, maps, /*tol=*/1e-11, /*max_iter=*/200);
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ASSERT_TRUE(res.converged)
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<< "Newton did not converge; ||G||=" << res.grad_inf_norm;
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// Realised geometric opposite-corner-angle sum = φ_e + G_e(x*) (= α_opp+α_opp).
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auto Gf = euclidean_gradient(mesh, res.x, maps);
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const double realised = maps.phi_e[circular] + Gf[ie];
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EXPECT_NEAR(phi_target, realised, 1e-9)
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<< "prescribed circular edge turn angle π−0.1 not realised at the solution";
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}
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