/* * \file jama_lu.h * \brief jama_lu.h */ #ifndef IMPALGEBRA_JAMA_LU_H #define IMPALGEBRA_JAMA_LU_H #include #include //for min(), max() below IMPALGEBRA_BEGIN_INTERNAL_NAMESPACE IMP_CLANG_PRAGMA(diagnostic ignored "-Wshadow") namespace JAMA { using namespace TNT; using namespace std; /** LU Decomposition.

For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n unit lower triangular matrix L, an n-by-n upper triangular matrix U, and a permutation vector piv of length m so that A(piv,:) = L*U. If m < n, then L is m-by-m and U is m-by-n.

The LU decompostion with pivoting always exists, even if the matrix is singular, so the constructor will never fail. The primary use of the LU decomposition is in the solution of square systems of simultaneous linear equations. This will fail if isNonsingular() returns false. */ template class LU { /* Array for internal storage of decomposition. */ Array2D LU_; int m, n, pivsign; Array1D piv; Array2D permute_copy(const Array2D &A, const Array1D &piv, int j0, int j1) { int piv_length = piv.dim(); Array2D X(piv_length, j1-j0+1); for (int i = 0; i < piv_length; i++) for (int j = j0; j <= j1; j++) X[i][j-j0] = A[piv[i]][j]; return X; } Array1D permute_copy(const Array1D &A, const Array1D &piv) { int piv_length = piv.dim(); if (piv_length != A.dim()) return Array1D(); Array1D x(piv_length); for (int i = 0; i < piv_length; i++) x[i] = A[piv[i]]; return x; } public : /** LU Decomposition @param A Rectangular matrix @return LU Decomposition object to access L, U and piv. */ LU (const Array2D &A) : LU_(A.copy()), m(A.dim1()), n(A.dim2()), piv(A.dim1()) { // Use a "left-looking", dot-product, Crout/Doolittle algorithm. for (int i = 0; i < m; i++) { piv[i] = i; } pivsign = 1; Real *LUrowi = 0;; Array1D LUcolj(m); // Outer loop. for (int j = 0; j < n; j++) { // Make a copy of the j-th column to localize references. for (int i = 0; i < m; i++) { LUcolj[i] = LU_[i][j]; } // Apply previous transformations. for (int i = 0; i < m; i++) { LUrowi = LU_[i]; // Most of the time is spent in the following dot product. int kmax = min(i,j); double s = 0.0; for (int k = 0; k < kmax; k++) { s += LUrowi[k]*LUcolj[k]; } LUrowi[j] = LUcolj[i] -= s; } // Find pivot and exchange if necessary. int p = j; for (int i = j+1; i < m; i++) { if (abs(LUcolj[i]) > abs(LUcolj[p])) { p = i; } } if (p != j) { int k=0; for (k = 0; k < n; k++) { double t = LU_[p][k]; LU_[p][k] = LU_[j][k]; LU_[j][k] = t; } k = piv[p]; piv[p] = piv[j]; piv[j] = k; pivsign = -pivsign; } // Compute multipliers. if ((j < m) && (LU_[j][j] != 0.0)) { for (int i = j+1; i < m; i++) { LU_[i][j] /= LU_[j][j]; } } } } /** Is the matrix nonsingular? @return 1 (true) if upper triangular factor U (and hence A) is nonsingular, 0 otherwise. */ int isNonsingular () { for (int j = 0; j < n; j++) { if (LU_[j][j] == 0) return 0; } return 1; } /** Return lower triangular factor @return L */ Array2D getL () { Array2D L_(m,n); for (int i = 0; i < m; i++) { for (int j = 0; j < n; j++) { if (i > j) { L_[i][j] = LU_[i][j]; } else if (i == j) { L_[i][j] = 1.0; } else { L_[i][j] = 0.0; } } } return L_; } /** Return upper triangular factor @return U portion of LU factorization. */ Array2D getU () { Array2D U_(n,n); for (int i = 0; i < n; i++) { for (int j = 0; j < n; j++) { if (i <= j) { U_[i][j] = LU_[i][j]; } else { U_[i][j] = 0.0; } } } return U_; } /** Return pivot permutation vector @return piv */ Array1D getPivot () { return piv; } /** Compute determinant using LU factors. @return determinant of A, or 0 if A is not square. */ Real det () { if (m != n) { return Real(0); } Real d = Real(pivsign); for (int j = 0; j < n; j++) { d *= LU_[j][j]; } return d; } /** Solve A*X = B @param B A Matrix with as many rows as A and any number of columns. @return X so that L*U*X = B(piv,:), if B is nonconformant, returns 0x0 (null) array. */ Array2D solve (const Array2D &B) { /* Dimensions: A is mxn, X is nxk, B is mxk */ if (B.dim1() != m) { return Array2D(0,0); } if (!isNonsingular()) { return Array2D(0,0); } // Copy right hand side with pivoting int nx = B.dim2(); Array2D X = permute_copy(B, piv, 0, nx-1); // Solve L*Y = B(piv,:) for (int k = 0; k < n; k++) { for (int i = k+1; i < n; i++) { for (int j = 0; j < nx; j++) { X[i][j] -= X[k][j]*LU_[i][k]; } } } // Solve U*X = Y; for (int k = n-1; k >= 0; k--) { for (int j = 0; j < nx; j++) { X[k][j] /= LU_[k][k]; } for (int i = 0; i < k; i++) { for (int j = 0; j < nx; j++) { X[i][j] -= X[k][j]*LU_[i][k]; } } } return X; } /** Solve A*x = b, where x and b are vectors of length equal to the number of rows in A. @param b a vector (Array1D> of length equal to the first dimension of A. @return x a vector (Array1D> so that L*U*x = b(piv), if B is nonconformant, returns 0x0 (null) array. */ Array1D solve (const Array1D &b) { /* Dimensions: A is mxn, X is nxk, B is mxk */ if (b.dim1() != m) { return Array1D(); } if (!isNonsingular()) { return Array1D(); } Array1D x = permute_copy(b, piv); // Solve L*Y = B(piv) for (int k = 0; k < n; k++) { for (int i = k+1; i < n; i++) { x[i] -= x[k]*LU_[i][k]; } } // Solve U*X = Y; for (int k = n-1; k >= 0; k--) { x[k] /= LU_[k][k]; for (int i = 0; i < k; i++) x[i] -= x[k]*LU_[i][k]; } return x; } }; /* class LU */ } /* namespace JAMA */ IMPALGEBRA_END_INTERNAL_NAMESPACE #endif /* IMPALGEBRA_JAMA_LU_H */