solvePMD {snQTL} | R Documentation |
Solving symmetric Penalized Matrix Decomposition
Description
An iterative algorithm that solves the Sparse Principal Component Analysis problem: given a positive definite matrix A:
max_{v} v^T A v
subject to
||v||_2 \leq 1, ||v||_1 \leq s
The solution v is the sparse leading eigenvector, and the corresponding objective
v^T A v
is the sparse leading eigenvalue.
Usage
solvePMD(x, sumabsv, v, niter = 50, trace = TRUE)
Arguments
x |
p-by-p matrix, symmetric and positive definite |
sumabsv |
the upperbound of the L_1 norm of |
v |
the starting value of the algorithm. |
niter |
number of iterations to perform the iterative optimizations |
trace |
whether to print tracing info during optimization |
Value
A list containing the following components:
v |
the sparse leading eigenvector v |
d |
the sparse leading eigenvalue |
v.init |
the initial value of v |
See Also
symmPMD()
.
[Package snQTL version 0.2 Index]