TetraQualityUtils.hpp

#pragma once
 
#include <Eigen/Core>
#include <optional>
 
namespace wmtk {
 
double newton_method_from_stack(
    const double t,
    std::vector<std::array<double, 12>>& assembles,
    std::function<Eigen::Vector3d(const double t)> param,
    std::function<double(const std::array<double, 12>&)> compute_energy,
    std::function<double(const std::array<double, 12>&)> compute_jacobian,
    std::function<double(const std::array<double, 12>&)> compute_hessian);
 
 
Eigen::Vector2d newton_method_from_stack(
    const Eigen::Vector2d& uv,
    std::vector<std::array<double, 12>>& assembles,
    std::function<Eigen::Vector3d(const Eigen::Vector2d& uv)> param,
    std::function<double(const std::array<double, 12>&)> compute_energy,
    std::function<void(const std::array<double, 12>&, Eigen::Vector2d&)> compute_jacobian,
    std::function<void(const std::array<double, 12>&, Eigen::Matrix2d&)> compute_hessian);
 
Eigen::Vector3d newton_method_from_stack(
    std::vector<std::array<double, 12>>& stack,
    std::function<double(const std::array<double, 12>&)> energy,
    std::function<void(const std::array<double, 12>&, Eigen::Vector3d&)> jacobian,
    std::function<void(const std::array<double, 12>&, Eigen::Matrix3d&)> hessian);
 
Eigen::Vector3d gradient_descent_from_stack(
    std::vector<std::array<double, 12>>& stack,
    std::function<double(const std::array<double, 12>&)> energy,
    std::function<void(const std::array<double, 12>&, Eigen::Vector3d&)> jacobian);
std::array<size_t, 4> orient_preserve_tet_reorder(const std::array<size_t, 4>& tetra, size_t v0);
 
// TODO: Add documentation
Eigen::Vector3d try_project(
    const Eigen::Vector3d& point,
    const std::vector<std::array<double, 9>>& assembled_neighbor);
} // namespace wmtk