| initial.convex {rifle} | R Documentation |
Convex Relaxation for Sparse GEP
Description
Estimate the K-dimensional subspace spanned by the largest K generalized eigenvector by solving a convex relaxation. The details are given in Tan et al. (2018).
Usage
initial.convex(A, B, lambda, K, nu = 1, epsilon = 0.005, maxiter = 1000, trace = FALSE)
Arguments
A |
Input the matrix A for sparse generalized eigenvalue problem. |
B |
Input the matrix B for sparse generalized eigenvalue problem. |
lambda |
A positive tuning parameter that constraints the solution to be sparse |
K |
A positive integer tuning parameter that constraints the solution to be low rank. |
nu |
An ADMM tuning parameter that controls the convergence of the ADMM algorithm. |
epsilon |
Threshold for convergence. Default value is 0.005. |
maxiter |
Maximum number of iterations. Default is 1000 iterations. |
trace |
Default value of trace=FALSE. If trace=TRUE, each iteration of the ADMM algorithm is printed. |
Value
Pi |
Estimated subspace Pi |
Author(s)
Kean Ming Tan
References
Sparse Generalized Eigenvalue Problewm: Optimal Statistical Rates via Truncated Rayleigh Flow", by Tan et al. (2018). To appear in Journal of the Royal Statistical Society: Series B. https://arxiv.org/pdf/1604.08697.pdf.