RDKit
Open-source cheminformatics and machine learning.
RDNumeric::EigenSolvers Namespace Reference

Functions

bool powerEigenSolver (unsigned int numEig, DoubleSymmMatrix &mat, DoubleVector &eigenValues, DoubleMatrix *eigenVectors=0, int seed=-1)
 Compute the numEig largest eigenvalues and, optionally, the. More...
 
static bool powerEigenSolver (unsigned int numEig, DoubleSymmMatrix &mat, DoubleVector &eigenValues, DoubleMatrix &eigenVectors, int seed=-1)
 

Function Documentation

bool RDNumeric::EigenSolvers::powerEigenSolver ( unsigned int  numEig,
DoubleSymmMatrix mat,
DoubleVector eigenValues,
DoubleMatrix eigenVectors = 0,
int  seed = -1 
)

Compute the numEig largest eigenvalues and, optionally, the.

eigenvectors.

Parameters
numEigthe number of eigenvalues we are interested in
matsymmetric input matrix of dimension N*N
eigenValuesVector used to return the eigenvalues (size = numEig)
eigenVectorsOptional matrix used to return the eigenvectors (size = N*numEig)
seedOptional values to seed the random value generator used to initialize the eigen vectors
Returns
a boolean indicating whether or not the calculation converged.

Notes:

  • The matrix, mat, is changed in this function

Algorithm:

We use the iterative power method, which works like this:

 u = arbitrary unit vector
 tol = 0.001
 currEigVal = 0.0;
 prevEigVal = -1.0e100
 while (abs(currEigVal - prevEigVal) > tol) :
     v = Au
     prevEigVal = currEigVal
     currEigVal = v[i] // where i is the id os the largest absolute component
     u = c*v

Referenced by powerEigenSolver().

static bool RDNumeric::EigenSolvers::powerEigenSolver ( unsigned int  numEig,
DoubleSymmMatrix mat,
DoubleVector eigenValues,
DoubleMatrix eigenVectors,
int  seed = -1 
)
inlinestatic

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Definition at line 59 of file PowerEigenSolver.h.

References powerEigenSolver().