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Replaces the O(n·F) full-FD Hessian with an O(F·36) block-local variant
that exploits the per-face locality of the hyper-ideal functional. Both
variants are kept (full-FD as correctness reference, block-FD as default)
and proven to match to FD rounding tolerance on all test configurations.
Java parity note
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HyperIdealFunctional.java line 295-298 declares:
public boolean hasHessian() { return false; }
i.e. the upstream Java functional has NO Hessian implementation, analytic
or numerical. Both Hessian variants in this file are conformallab++
extensions beyond the Java port. Analytic Hessian via Schläfli-type
differentiation through (b_i, a_e) → l_ij → ζ_13/ζ_14/ζ_15 → α_ij/β_i
is deferred to a future PR.
Implementation
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* code/include/hyper_ideal_functional.hpp
- New pure-math helper face_angles_from_local_dofs() takes 6 input DOFs
(b1, b2, b3, a12, a23, a31) + variability flags and returns the 6
output angles (β1, β2, β3, α12, α23, α31).
- Used by block-FD Hessian as the inner loop; identical semantics to
the existing compute_face_angles().
* code/include/hyper_ideal_hessian.hpp
- hyper_ideal_hessian_block_fd() — new, default production path
- hyper_ideal_hessian_block_fd_sym() — symmetrised variant
- hyper_ideal_hessian() — full-FD baseline, kept for cross-validation
- hyper_ideal_hessian_sym() — symmetrised baseline
- Header docblock documents speed-up curve: ~33× at cathead.obj scale,
~1166× at brezel.obj scale.
Tests (7 new in test_hyper_ideal_hessian.cpp)
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* PureHelperMatchesMeshHelper — refactor sanity
* BlockFD_MatchesFullFD_ClosedTetrahedron
* BlockFD_MatchesFullFD_Open3FaceMesh (boundary edge path)
* BlockFD_MatchesFullFD_PinnedDOFs (partial-DOF path)
* BlockFD_IsPSD (Springborn 2020 convexity)
* BlockFD_SparsityMatchesFaceAdjacency (structural correctness)
* BlockFD_FasterThanFullFD (performance assertion: ≥ 3×)
Measured speed-up on the 200-face tet strip (603 DOFs):
full-FD: 226 591 µs
block-FD: 2 347 µs
ratio: 96.5×
The assertion uses ≥ 3× to leave wide CI-hardware tolerance.
Test count
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CGAL suite: 184 → 191 (+7). Zero skips.
Why not full analytic now
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Full analytic Hessian via the chain rule
(b_i, a_e) → l_ij → ζ_{13,14,15} → α_ij / β_i
requires Schläfli-type differentiation with multiple cases for the
ideal / hyper-ideal vertex mix. It would add another ~6× over
block-FD but at significantly higher implementation and verification
cost. Block-FD already removes the practical bottleneck for meshes
up to ~10k faces; analytic optimisation can land later when justified
by a concrete profiling result.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>