#ifndef JAMA_CHOLESKY_H #define JAMA_CHOLESKY_H #include "math.h" /* needed for sqrt() below. */ namespace JAMA { using namespace TNT; /**

For a symmetric, positive definite matrix A, this function computes the Cholesky factorization, i.e. it computes a lower triangular matrix L such that A = L*L'. If the matrix is not symmetric or positive definite, the function computes only a partial decomposition. This can be tested with the is_spd() flag.

Typical usage looks like:

	Array2D A(n,n);
	Array2D L;

	 ... 

	Cholesky chol(A);

	if (chol.is_spd())
		L = chol.getL();
		
  	else
		cout << "factorization was not complete.\n";

	

(Adapted from JAMA, a Java Matrix Library, developed by jointly by the Mathworks and NIST; see http://math.nist.gov/javanumerics/jama). */ template class Cholesky { Array2D L_; // lower triangular factor int isspd; // 1 if matrix to be factored was SPD public: Cholesky(); Cholesky(const Array2D &A); Array2D getL() const; Array1D solve(const Array1D &B); Array2D solve(const Array2D &B); int is_spd() const; }; template Cholesky::Cholesky() : L_(0,0), isspd(0) {} /** @return 1, if original matrix to be factored was symmetric positive-definite (SPD). */ template int Cholesky::is_spd() const { return isspd; } /** @return the lower triangular factor, L, such that L*L'=A. */ template Array2D Cholesky::getL() const { return L_; } /** Constructs a lower triangular matrix L, such that L*L'= A. If A is not symmetric positive-definite (SPD), only a partial factorization is performed. If is_spd() evalutate true (1) then the factorizaiton was successful. */ template Cholesky::Cholesky(const Array2D &A) { int m = A.dim1(); int n = A.dim2(); isspd = (m == n); if (m != n) { L_ = Array2D(0,0); return; } L_ = Array2D(n,n); // Main loop. for (int j = 0; j < n; j++) { Real d(0.0); for (int k = 0; k < j; k++) { Real s(0.0); for (int i = 0; i < k; i++) { s += L_[k][i]*L_[j][i]; } L_[j][k] = s = (A[j][k] - s)/L_[k][k]; d = d + s*s; isspd = isspd && (A[k][j] == A[j][k]); } d = A[j][j] - d; isspd = isspd && (d > 0.0); L_[j][j] = sqrt(d > 0.0 ? d : 0.0); for (int k = j+1; k < n; k++) { L_[j][k] = 0.0; } } } /** Solve a linear system A*x = b, using the previously computed cholesky factorization of A: L*L'. @param B A Matrix with as many rows as A and any number of columns. @return x so that L*L'*x = b. If b is nonconformat, or if A was not symmetric posidtive definite, a null (0x0) array is returned. */ template Array1D Cholesky::solve(const Array1D &b) { int n = L_.dim1(); if (b.dim1() != n) return Array1D(); Array1D x = b.copy(); // Solve L*y = b; for (int k = 0; k < n; k++) { for (int i = 0; i < k; i++) x[k] -= x[i]*L_[k][i]; x[k] /= L_[k][k]; } // Solve L'*X = Y; for (int k = n-1; k >= 0; k--) { for (int i = k+1; i < n; i++) x[k] -= x[i]*L_[i][k]; x[k] /= L_[k][k]; } return x; } /** Solve a linear system A*X = B, using the previously computed cholesky factorization of A: L*L'. @param B A Matrix with as many rows as A and any number of columns. @return X so that L*L'*X = B. If B is nonconformat, or if A was not symmetric posidtive definite, a null (0x0) array is returned. */ template Array2D Cholesky::solve(const Array2D &B) { int n = L_.dim1(); if (B.dim1() != n) return Array2D(); Array2D X = B.copy(); int nx = B.dim2(); // Cleve's original code #if 0 // Solve L*Y = B; 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]*L_[k][i]; } } for (int j = 0; j < nx; j++) { X[k][j] /= L_[k][k]; } } // Solve L'*X = Y; for (int k = n-1; k >= 0; k--) { for (int j = 0; j < nx; j++) { X[k][j] /= L_[k][k]; } for (int i = 0; i < k; i++) { for (int j = 0; j < nx; j++) { X[i][j] -= X[k][j]*L_[k][i]; } } } #endif // Solve L*y = b; for (int j=0; j< nx; j++) { for (int k = 0; k < n; k++) { for (int i = 0; i < k; i++) X[k][j] -= X[i][j]*L_[k][i]; X[k][j] /= L_[k][k]; } } // Solve L'*X = Y; for (int j=0; j= 0; k--) { for (int i = k+1; i < n; i++) X[k][j] -= X[i][j]*L_[i][k]; X[k][j] /= L_[k][k]; } } return X; } } // namespace JAMA #endif // JAMA_CHOLESKY_H