RDKit
Open-source cheminformatics and machine learning.
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Functions | |
bool RDKIT_EIGENSOLVERS_EXPORT | powerEigenSolver (unsigned int numEig, DoubleSymmMatrix &mat, DoubleVector &eigenValues, DoubleMatrix *eigenVectors=nullptr, int seed=-1) |
static bool | powerEigenSolver (unsigned int numEig, DoubleSymmMatrix &mat, DoubleVector &eigenValues, DoubleMatrix &eigenVectors, int seed=-1) |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. | |
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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 60 of file PowerEigenSolver.h.
References powerEigenSolver().
bool RDKIT_EIGENSOLVERS_EXPORT RDNumeric::EigenSolvers::powerEigenSolver | ( | unsigned int | numEig, |
DoubleSymmMatrix & | mat, | ||
DoubleVector & | eigenValues, | ||
DoubleMatrix * | eigenVectors = nullptr , |
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int | seed = -1 |
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Compute the numEig
largest eigenvalues and, optionally, the corresponding eigenvectors.
numEig | the number of eigenvalues we are interested in |
mat | symmetric input matrix of dimension N*N |
eigenValues | Vector used to return the eigenvalues (size = numEig) |
eigenVectors | Optional matrix used to return the eigenvectors (size = N*numEig) |
seed | Optional values to seed the random value generator used to initialize the eigen vectors |
Notes:
mat
, is changed in this functionAlgorithm:
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 of the largest absolute component u = c*v
Referenced by powerEigenSolver().