Package org.drip.sample.matrix

Cholesky Factorization, PCA, and Eigenization

Author:

Lakshmi Krishnamurthy

Class Summary

Class	Description
CholeskyFactorization	CholeskyFactorization demonstrates the Cholesky Factorization and Transpose Reconciliation of the Input Matrix.
Eigenization	Eigenization demonstrates how to generate the eigenvalue and eigenvector for the Input Matrix.
GrahamSchmidtProcess	GrahamSchmidtProcess illustrates the Graham Schmidt Orthogonalization and Orthonormalization.
LinearAlgebra	LinearAlgebra implements Samples for Linear Algebra and Matrix Manipulations.
MultivariateRandom	MultivariateRandom demonstrates the Technique to generate Correlated Multivariate Random Variables using Cholesky Factorial Method.
PrincipalComponent	PrincipalComponent demonstrates how to generate the Principal eigenvalue and eigenvector for the Input Matrix.
QRDecomposition	QRDecomposition demonstrates the technique to perform a QR Decomposition of the Input Square Matrix into an Orthogonal and an Upper Triangular Counterparts.
RayleighQuotient	RayleighQuotient demonstrates the Computation of an Approximate to the Eigenvalue using the Rayleigh Quotient.
SylvesterInterpolantReconciler	SylvesterInterpolantReconciler demonstrates the Construction and Usage of the Sylvester Matrix Interpolant.