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Diffstat (limited to 'thirdparty/bullet/src/LinearMath/btPolarDecomposition.cpp')
-rw-r--r-- | thirdparty/bullet/src/LinearMath/btPolarDecomposition.cpp | 98 |
1 files changed, 98 insertions, 0 deletions
diff --git a/thirdparty/bullet/src/LinearMath/btPolarDecomposition.cpp b/thirdparty/bullet/src/LinearMath/btPolarDecomposition.cpp new file mode 100644 index 0000000000..b3664faa4e --- /dev/null +++ b/thirdparty/bullet/src/LinearMath/btPolarDecomposition.cpp @@ -0,0 +1,98 @@ +#include "btPolarDecomposition.h" +#include "btMinMax.h" + +namespace +{ + btScalar abs_column_sum(const btMatrix3x3& a, int i) + { + return btFabs(a[0][i]) + btFabs(a[1][i]) + btFabs(a[2][i]); + } + + btScalar abs_row_sum(const btMatrix3x3& a, int i) + { + return btFabs(a[i][0]) + btFabs(a[i][1]) + btFabs(a[i][2]); + } + + btScalar p1_norm(const btMatrix3x3& a) + { + const btScalar sum0 = abs_column_sum(a,0); + const btScalar sum1 = abs_column_sum(a,1); + const btScalar sum2 = abs_column_sum(a,2); + return btMax(btMax(sum0, sum1), sum2); + } + + btScalar pinf_norm(const btMatrix3x3& a) + { + const btScalar sum0 = abs_row_sum(a,0); + const btScalar sum1 = abs_row_sum(a,1); + const btScalar sum2 = abs_row_sum(a,2); + return btMax(btMax(sum0, sum1), sum2); + } +} + + + +btPolarDecomposition::btPolarDecomposition(btScalar tolerance, unsigned int maxIterations) +: m_tolerance(tolerance) +, m_maxIterations(maxIterations) +{ +} + +unsigned int btPolarDecomposition::decompose(const btMatrix3x3& a, btMatrix3x3& u, btMatrix3x3& h) const +{ + // Use the 'u' and 'h' matrices for intermediate calculations + u = a; + h = a.inverse(); + + for (unsigned int i = 0; i < m_maxIterations; ++i) + { + const btScalar h_1 = p1_norm(h); + const btScalar h_inf = pinf_norm(h); + const btScalar u_1 = p1_norm(u); + const btScalar u_inf = pinf_norm(u); + + const btScalar h_norm = h_1 * h_inf; + const btScalar u_norm = u_1 * u_inf; + + // The matrix is effectively singular so we cannot invert it + if (btFuzzyZero(h_norm) || btFuzzyZero(u_norm)) + break; + + const btScalar gamma = btPow(h_norm / u_norm, 0.25f); + const btScalar inv_gamma = btScalar(1.0) / gamma; + + // Determine the delta to 'u' + const btMatrix3x3 delta = (u * (gamma - btScalar(2.0)) + h.transpose() * inv_gamma) * btScalar(0.5); + + // Update the matrices + u += delta; + h = u.inverse(); + + // Check for convergence + if (p1_norm(delta) <= m_tolerance * u_1) + { + h = u.transpose() * a; + h = (h + h.transpose()) * 0.5; + return i; + } + } + + // The algorithm has failed to converge to the specified tolerance, but we + // want to make sure that the matrices returned are in the right form. + h = u.transpose() * a; + h = (h + h.transpose()) * 0.5; + + return m_maxIterations; +} + +unsigned int btPolarDecomposition::maxIterations() const +{ + return m_maxIterations; +} + +unsigned int polarDecompose(const btMatrix3x3& a, btMatrix3x3& u, btMatrix3x3& h) +{ + static btPolarDecomposition polar; + return polar.decompose(a, u, h); +} + |