Rework rank-detection tolerance in pseudoinverse

matrix/PseudoInverse.hxx

@@ -12,32 +12,20 @@
  * Full rank Cholesky factorization of A
  */
 template<typename Type>
-void fullRankCholeskyTolerance(Type &tol)
+Type typeEpsilon();
-{
-    tol /= 10000000;
-}
 
 template<> inline
-void fullRankCholeskyTolerance<double>(double &tol)
+float typeEpsilon<float>()
 {
-    tol /= 1000000000000000000.0;
+    return FLT_EPSILON;
 }
 
 template<typename Type, size_t N>
 SquareMatrix<Type, N> fullRankCholesky(const SquareMatrix<Type, N> & A,
                                size_t& rank)
 {
-    // Compute
+    // Loses one ulp accuracy per row of diag, relative to largest magnitude
-    // dA = np.diag(A)
+    const Type tol = N * typeEpsilon<Type>() * A.diag().max();
-    // tol = np.min(dA[dA > 0]) * 1e-9
-    Vector<Type, N> d = A.diag();
-    Type tol = d.max();
-    for (size_t k = 0; k < N; k++) {
-        if ((d(k) > 0) && (d(k) < tol)) {
-            tol = d(k);
-        }
-    }
-    fullRankCholeskyTolerance<Type>(tol);
 
     Matrix<Type, N, N> L;
 
@@ -59,7 +47,6 @@ SquareMatrix<Type, N> fullRankCholesky(const SquareMatrix<Type, N> & A,
                 L(i, r) = A(i, k) - LL;
             }
         }
-
         if (L(k, r) > tol) {
             L(k, r) = sqrt(L(k, r));
 

test/pseudoInverse.cpp

@@ -127,28 +127,26 @@ int main()
     TEST((retM1 - retM_check).abs().max() < 1e-5);
     TEST((retN1 - retN_check).abs().max() < 1e-5);
 
-    float float_scale = 1.f;
-    fullRankCholeskyTolerance(float_scale);
-    double double_scale = 1.;
-    fullRankCholeskyTolerance(double_scale);
-    TEST(static_cast<double>(float_scale) > double_scale);
-
     // Real-world test case
-    const float real_alloc[5][6] = {{ 0.794079,  0.794079,  0.794079,  0.794079,  0.0000,  0.0000},
+    const float real_alloc[5][6] = {
+        { 0.794079,  0.794079,  0.794079,  0.794079,  0.0000,  0.0000},
         { 0.607814,  0.607814,  0.607814,  0.607814,  1.0000,  1.0000},
         {-0.672516,  0.915642, -0.915642,  0.672516,  0.0000,  0.0000},
         { 0.159704,  0.159704,  0.159704,  0.159704, -0.2500, -0.2500},
-                                    { 0.607814, -0.607814,  0.607814, -0.607814,  1.0000,  1.0000}};
+        { 0.607814, -0.607814,  0.607814, -0.607814,  1.0000,  1.0000}
+    };
     Matrix<float, 5, 6> real ( real_alloc);
     Matrix<float, 6, 5> real_pinv = geninv(real);
 
     // from SVD-based inverse
-    const float real_pinv_expected_alloc[6][5] = {{ 2.096205,  -2.722267,   2.056547,   1.503279,   3.098087},
+    const float real_pinv_expected_alloc[6][5] = {
+        { 2.096205,  -2.722267,   2.056547,   1.503279,   3.098087},
         { 1.612621,  -1.992694,   2.056547,   1.131090,   2.275467},
         {-1.062688,   2.043479,  -2.056547,  -0.927950,  -2.275467},
         {-1.546273,   2.773052,  -2.056547,  -1.300139,  -3.098087},
         {-0.293930,   0.443445,   0.000000,  -0.226222,   0.000000},
-                                                  {-0.293930,   0.443445,   0.000000,  -0.226222,   0.000000}};
+        {-0.293930,   0.443445,   0.000000,  -0.226222,   0.000000}
+    };
     Matrix<float, 6, 5> real_pinv_expected(real_pinv_expected_alloc);
     TEST((real_pinv - real_pinv_expected).abs().max() < 1e-4);