1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla
7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed
8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifndef EIGEN_INVERSE_H
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_INVERSE_H
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace Eigen {
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal {
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/**********************************
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath*** General case implementation ***
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath**********************************/
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline void run(const MatrixType& matrix, ResultType& result)
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result = matrix.partialPivLu().inverse();
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse_and_det_with_check { /* nothing! general case not supported. */ };
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/****************************
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath*** Size 1 implementation ***
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath****************************/
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse<MatrixType, ResultType, 1>
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline void run(const MatrixType& matrix, ResultType& result)
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typedef typename MatrixType::Scalar Scalar;
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(0,0) = Scalar(1) / matrix.coeff(0,0);
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse_and_det_with_check<MatrixType, ResultType, 1>
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline void run(
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const MatrixType& matrix,
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const typename MatrixType::RealScalar& absDeterminantThreshold,
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ResultType& result,
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typename ResultType::Scalar& determinant,
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    bool& invertible
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  )
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    determinant = matrix.coeff(0,0);
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    invertible = abs(determinant) > absDeterminantThreshold;
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    if(invertible) result.coeffRef(0,0) = typename ResultType::Scalar(1) / determinant;
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/****************************
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath*** Size 2 implementation ***
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath****************************/
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline void compute_inverse_size2_helper(
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const MatrixType& matrix, const typename ResultType::Scalar& invdet,
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ResultType& result)
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(0,0) = matrix.coeff(1,1) * invdet;
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(1,0) = -matrix.coeff(1,0) * invdet;
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(0,1) = -matrix.coeff(0,1) * invdet;
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(1,1) = matrix.coeff(0,0) * invdet;
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse<MatrixType, ResultType, 2>
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline void run(const MatrixType& matrix, ResultType& result)
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typedef typename ResultType::Scalar Scalar;
85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const Scalar invdet = typename MatrixType::Scalar(1) / matrix.determinant();
86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    compute_inverse_size2_helper(matrix, invdet, result);
87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse_and_det_with_check<MatrixType, ResultType, 2>
92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline void run(
94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const MatrixType& matrix,
95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const typename MatrixType::RealScalar& absDeterminantThreshold,
96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ResultType& inverse,
97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typename ResultType::Scalar& determinant,
98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    bool& invertible
99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  )
100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typedef typename ResultType::Scalar Scalar;
102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    determinant = matrix.determinant();
103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    invertible = abs(determinant) > absDeterminantThreshold;
104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    if(!invertible) return;
105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const Scalar invdet = Scalar(1) / determinant;
106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    compute_inverse_size2_helper(matrix, invdet, inverse);
107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/****************************
111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath*** Size 3 implementation ***
112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath****************************/
113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, int i, int j>
115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline typename MatrixType::Scalar cofactor_3x3(const MatrixType& m)
116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  enum {
118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    i1 = (i+1) % 3,
119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    i2 = (i+2) % 3,
120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    j1 = (j+1) % 3,
121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    j2 = (j+2) % 3
122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  };
123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  return m.coeff(i1, j1) * m.coeff(i2, j2)
124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath       - m.coeff(i1, j2) * m.coeff(i2, j1);
125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline void compute_inverse_size3_helper(
129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const MatrixType& matrix,
130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const typename ResultType::Scalar& invdet,
131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const Matrix<typename ResultType::Scalar,3,1>& cofactors_col0,
132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ResultType& result)
133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.row(0) = cofactors_col0 * invdet;
135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(1,0) =  cofactor_3x3<MatrixType,0,1>(matrix) * invdet;
136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(1,1) =  cofactor_3x3<MatrixType,1,1>(matrix) * invdet;
137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(1,2) =  cofactor_3x3<MatrixType,2,1>(matrix) * invdet;
138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(2,0) =  cofactor_3x3<MatrixType,0,2>(matrix) * invdet;
139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(2,1) =  cofactor_3x3<MatrixType,1,2>(matrix) * invdet;
140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  result.coeffRef(2,2) =  cofactor_3x3<MatrixType,2,2>(matrix) * invdet;
141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse<MatrixType, ResultType, 3>
145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline void run(const MatrixType& matrix, ResultType& result)
147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typedef typename ResultType::Scalar Scalar;
149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Matrix<typename MatrixType::Scalar,3,1> cofactors_col0;
150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    cofactors_col0.coeffRef(0) =  cofactor_3x3<MatrixType,0,0>(matrix);
151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    cofactors_col0.coeffRef(1) =  cofactor_3x3<MatrixType,1,0>(matrix);
152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    cofactors_col0.coeffRef(2) =  cofactor_3x3<MatrixType,2,0>(matrix);
153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const Scalar det = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const Scalar invdet = Scalar(1) / det;
155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, result);
156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
159c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
160c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse_and_det_with_check<MatrixType, ResultType, 3>
161c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
162c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline void run(
163c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const MatrixType& matrix,
164c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const typename MatrixType::RealScalar& absDeterminantThreshold,
165c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ResultType& inverse,
166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typename ResultType::Scalar& determinant,
167c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    bool& invertible
168c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  )
169c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
170c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typedef typename ResultType::Scalar Scalar;
171c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Matrix<Scalar,3,1> cofactors_col0;
172c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    cofactors_col0.coeffRef(0) =  cofactor_3x3<MatrixType,0,0>(matrix);
173c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    cofactors_col0.coeffRef(1) =  cofactor_3x3<MatrixType,1,0>(matrix);
174c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    cofactors_col0.coeffRef(2) =  cofactor_3x3<MatrixType,2,0>(matrix);
175c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    determinant = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
176c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    invertible = abs(determinant) > absDeterminantThreshold;
177c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    if(!invertible) return;
178c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const Scalar invdet = Scalar(1) / determinant;
179c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, inverse);
180c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
181c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
182c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
183c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/****************************
184c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath*** Size 4 implementation ***
185c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath****************************/
186c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
187c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived>
188c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline const typename Derived::Scalar general_det3_helper
189c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath(const MatrixBase<Derived>& matrix, int i1, int i2, int i3, int j1, int j2, int j3)
190c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
191c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  return matrix.coeff(i1,j1)
192c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath         * (matrix.coeff(i2,j2) * matrix.coeff(i3,j3) - matrix.coeff(i2,j3) * matrix.coeff(i3,j2));
193c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
194c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
195c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, int i, int j>
196c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline typename MatrixType::Scalar cofactor_4x4(const MatrixType& matrix)
197c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
198c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  enum {
199c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    i1 = (i+1) % 4,
200c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    i2 = (i+2) % 4,
201c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    i3 = (i+3) % 4,
202c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    j1 = (j+1) % 4,
203c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    j2 = (j+2) % 4,
204c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    j3 = (j+3) % 4
205c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  };
206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  return general_det3_helper(matrix, i1, i2, i3, j1, j2, j3)
207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath       + general_det3_helper(matrix, i2, i3, i1, j1, j2, j3)
208c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath       + general_det3_helper(matrix, i3, i1, i2, j1, j2, j3);
209c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
210c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
211c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<int Arch, typename Scalar, typename MatrixType, typename ResultType>
212c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse_size4
213c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
214c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static void run(const MatrixType& matrix, ResultType& result)
215c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
216c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(0,0) =  cofactor_4x4<MatrixType,0,0>(matrix);
217c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(1,0) = -cofactor_4x4<MatrixType,0,1>(matrix);
218c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(2,0) =  cofactor_4x4<MatrixType,0,2>(matrix);
219c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(3,0) = -cofactor_4x4<MatrixType,0,3>(matrix);
220c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(0,2) =  cofactor_4x4<MatrixType,2,0>(matrix);
221c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(1,2) = -cofactor_4x4<MatrixType,2,1>(matrix);
222c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(2,2) =  cofactor_4x4<MatrixType,2,2>(matrix);
223c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(3,2) = -cofactor_4x4<MatrixType,2,3>(matrix);
224c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(0,1) = -cofactor_4x4<MatrixType,1,0>(matrix);
225c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(1,1) =  cofactor_4x4<MatrixType,1,1>(matrix);
226c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(2,1) = -cofactor_4x4<MatrixType,1,2>(matrix);
227c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(3,1) =  cofactor_4x4<MatrixType,1,3>(matrix);
228c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(0,3) = -cofactor_4x4<MatrixType,3,0>(matrix);
229c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(1,3) =  cofactor_4x4<MatrixType,3,1>(matrix);
230c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(2,3) = -cofactor_4x4<MatrixType,3,2>(matrix);
231c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result.coeffRef(3,3) =  cofactor_4x4<MatrixType,3,3>(matrix);
232c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    result /= (matrix.col(0).cwiseProduct(result.row(0).transpose())).sum();
233c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
234c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
235c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
236c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
237c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse<MatrixType, ResultType, 4>
238c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : compute_inverse_size4<Architecture::Target, typename MatrixType::Scalar,
239c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                            MatrixType, ResultType>
240c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
241c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
242c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
243c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename ResultType>
244c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct compute_inverse_and_det_with_check<MatrixType, ResultType, 4>
245c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
246c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline void run(
247c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const MatrixType& matrix,
248c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const typename MatrixType::RealScalar& absDeterminantThreshold,
249c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ResultType& inverse,
250c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typename ResultType::Scalar& determinant,
251c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    bool& invertible
252c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  )
253c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
254c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    determinant = matrix.determinant();
255c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    invertible = abs(determinant) > absDeterminantThreshold;
256c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    if(invertible) compute_inverse<MatrixType, ResultType>::run(matrix, inverse);
257c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
258c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
259c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
260c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/*************************
261c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath*** MatrixBase methods ***
262c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath*************************/
263c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
264c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType>
265c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct traits<inverse_impl<MatrixType> >
266c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
267c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::PlainObject ReturnType;
268c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
269c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
270c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType>
271c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct inverse_impl : public ReturnByValue<inverse_impl<MatrixType> >
272c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
273c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::Index Index;
274c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename internal::eval<MatrixType>::type MatrixTypeNested;
275c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
276c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  MatrixTypeNested m_matrix;
277c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
278c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  inverse_impl(const MatrixType& matrix)
279c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    : m_matrix(matrix)
280c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {}
281c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
282c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  inline Index rows() const { return m_matrix.rows(); }
283c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  inline Index cols() const { return m_matrix.cols(); }
284c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
285c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  template<typename Dest> inline void evalTo(Dest& dst) const
286c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
287c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const int Size = EIGEN_PLAIN_ENUM_MIN(MatrixType::ColsAtCompileTime,Dest::ColsAtCompileTime);
288c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    EIGEN_ONLY_USED_FOR_DEBUG(Size);
289c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    eigen_assert(( (Size<=1) || (Size>4) || (extract_data(m_matrix)!=extract_data(dst)))
290c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath              && "Aliasing problem detected in inverse(), you need to do inverse().eval() here.");
291c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
292c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    compute_inverse<MatrixTypeNestedCleaned, Dest>::run(m_matrix, dst);
293c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
294c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
295c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
296c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace internal
297c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
298c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \lu_module
299c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
300c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \returns the matrix inverse of this matrix.
301c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
302c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * For small fixed sizes up to 4x4, this method uses cofactors.
303c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * In the general case, this method uses class PartialPivLU.
304c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
305c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \note This matrix must be invertible, otherwise the result is undefined. If you need an
306c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * invertibility check, do the following:
307c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \li for fixed sizes up to 4x4, use computeInverseAndDetWithCheck().
308c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \li for the general case, use class FullPivLU.
309c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
310c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Example: \include MatrixBase_inverse.cpp
311c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Output: \verbinclude MatrixBase_inverse.out
312c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
313c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \sa computeInverseAndDetWithCheck()
314c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  */
315c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived>
316c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline const internal::inverse_impl<Derived> MatrixBase<Derived>::inverse() const
317c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
318c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsInteger,THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
319c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  eigen_assert(rows() == cols());
320c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  return internal::inverse_impl<Derived>(derived());
321c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
322c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
323c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \lu_module
324c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
325c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Computation of matrix inverse and determinant, with invertibility check.
326c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
327c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * This is only for fixed-size square matrices of size up to 4x4.
328c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
329c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \param inverse Reference to the matrix in which to store the inverse.
330c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \param determinant Reference to the variable in which to store the inverse.
331c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
332c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
333c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *                                The matrix will be declared invertible if the absolute value of its
334c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *                                determinant is greater than this threshold.
335c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
336c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Example: \include MatrixBase_computeInverseAndDetWithCheck.cpp
337c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Output: \verbinclude MatrixBase_computeInverseAndDetWithCheck.out
338c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
339c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \sa inverse(), computeInverseWithCheck()
340c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  */
341c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived>
342c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename ResultType>
343c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline void MatrixBase<Derived>::computeInverseAndDetWithCheck(
344c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ResultType& inverse,
345c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typename ResultType::Scalar& determinant,
346c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    bool& invertible,
347c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const RealScalar& absDeterminantThreshold
348c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  ) const
349c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
350c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
351c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  eigen_assert(rows() == cols());
352c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // for 2x2, it's worth giving a chance to avoid evaluating.
353c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // for larger sizes, evaluating has negligible cost and limits code size.
354c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename internal::conditional<
355c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    RowsAtCompileTime == 2,
356c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typename internal::remove_all<typename internal::nested<Derived, 2>::type>::type,
357c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    PlainObject
358c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  >::type MatrixType;
359c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  internal::compute_inverse_and_det_with_check<MatrixType, ResultType>::run
360c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    (derived(), absDeterminantThreshold, inverse, determinant, invertible);
361c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
362c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
363c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \lu_module
364c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
365c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Computation of matrix inverse, with invertibility check.
366c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
367c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * This is only for fixed-size square matrices of size up to 4x4.
368c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
369c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \param inverse Reference to the matrix in which to store the inverse.
370c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
371c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
372c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *                                The matrix will be declared invertible if the absolute value of its
373c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *                                determinant is greater than this threshold.
374c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
375c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Example: \include MatrixBase_computeInverseWithCheck.cpp
376c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Output: \verbinclude MatrixBase_computeInverseWithCheck.out
377c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
378c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \sa inverse(), computeInverseAndDetWithCheck()
379c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  */
380c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived>
381c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename ResultType>
382c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline void MatrixBase<Derived>::computeInverseWithCheck(
383c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ResultType& inverse,
384c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    bool& invertible,
385c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const RealScalar& absDeterminantThreshold
386c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  ) const
387c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
388c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  RealScalar determinant;
389c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
390c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  eigen_assert(rows() == cols());
391c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  computeInverseAndDetWithCheck(inverse,determinant,invertible,absDeterminantThreshold);
392c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
393c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
394c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace Eigen
395c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
396c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif // EIGEN_INVERSE_H
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