10 float data[9] = {0, 2, 3,
14 float data_check[9] = {
31 for (
size_t i = 0; i <
n_large; i++) {
32 A_large_I =
inv(A_large);
41 -2, 1, 1, -1, -5, 1, 2, -1, 0,
42 -3, 2, -1, 0, 2, 2, -1, -5, 3,
43 0, 0, 0, 1, 4, -3, 3, 0, -2,
44 2, 2, -1, -2, -1, 0, 3, 0, 1,
45 -1, 2, -1, -1, -3, 3, 0, -2, 3,
46 0, 1, 1, -3, 3, -2, 0, -4, 0,
47 1, 0, 0, 0, 0, 0, -2, 4, -3,
48 1, -1, 0, -1, -1, 1, -1, -3, 4,
49 0, 3, -1, -2, 2, 1, -2, 0, -1
52 float data2_check[81] = {
53 6, -4, 3, -3, -9, -8, -10, 8, 14,
54 -2, -7, -5, -3, -2, -2, -16, -5, 8,
55 -2, 0, -23, 7, -24, -5, -28, -14, 9,
56 3, -7, 2, -5, -4, -6, -13, 4, 13,
57 -1, 4, -8, 5, -8, 0, -3, -5, -2,
58 6, 7, -7, 7, -21, -7, -5, 3, 6,
59 1, 4, -4, 4, -7, -1, 0, -1, -1,
60 -7, 3, -11, 5, 1, 6, -1, -13, -10,
61 -8, 0, -11, 3, 3, 6, -5, -14, -8
72 float data3_check[9] = {
73 -0.3333333f, -1.6666666f, 1,
105 1.33471626f, 0.74946721f, -0.0531679f,
106 0.74946721f, 1.07519593f, 0.08036323f,
107 -0.0531679f, 0.08036323f, 1.01618474f
111 float data4_cholesky[9] = {
112 1.15529921f, 0.f, 0.f,
113 0.6487213f, 0.80892311f, 0.f,
114 -0.04602089f, 0.13625271f, 0.99774847f
SquareMatrix< Type, M > cholesky(const SquareMatrix< Type, M > &A)
cholesky decomposition
bool isEqual(const Matrix< Type, M, N > &x, const Matrix< Type, M, N > &y, const Type eps=1e-4f)
Vector< float, 6 > f(float t, const Matrix< float, 6, 1 > &, const Matrix< float, 3, 1 > &)
bool inv(const SquareMatrix< Type, M > &A, SquareMatrix< Type, M > &inv)
inverse based on LU factorization with partial pivotting
SquareMatrix< Type, M > I() const
Dual< Scalar, N > max(const Dual< Scalar, N > &a, const Dual< Scalar, N > &b)
SquareMatrix< Type, M > choleskyInv(const SquareMatrix< Type, M > &A)
cholesky inverse
Dual< Scalar, N > abs(const Dual< Scalar, N > &a)
static const size_t n_large