slope_admm: Optimization solver
In mgerus/ABSLOPE: High-dimensional Model Selection with Missing Values
Description
Solve optimation with proximal gradient descent
Usage
1
2
slope_admm(A, b, z, u, lambda_seq, rho, max_iter = 500,
tol_infeas = 1e-08, verbose = FALSE)
Arguments
A
Weighted design matrix
b
Response
z
Initailization
u
Initailization
lambda_seq
Penalization coefficient
rho
Step-size
max_iter
Maximum iteration number
tol_infeas
Tolerance
verbose
If TRUE, print results in each iteration
mgerus/ABSLOPE documentation built on Nov. 13, 2019, 12:34 a.m.
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Description
Solve optimation with proximal gradient descent
Usage
1 2 | slope_admm(A, b, z, u, lambda_seq, rho, max_iter = 500,
tol_infeas = 1e-08, verbose = FALSE)
|
Arguments
A |
Weighted design matrix |
b |
Response |
z |
Initailization |
u |
Initailization |
lambda_seq |
Penalization coefficient |
rho |
Step-size |
max_iter |
Maximum iteration number |
tol_infeas |
Tolerance |
verbose |
If TRUE, print results in each iteration |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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