pg_optim: Proximal gradient optimization.
In tathagatabasu/bootlasso: Boot-strap Method for LASSO
Description
Usage
Arguments
Value
Description
Function for proximal-gradient optimization for non-differentiable functions
We're trying to minimize f + g; where f is the differentiable part and g is the non-differentiable part.
Usage
1
Arguments
x
Starting value.
f
Function to optimize.
df
Gradient of f.
pg
Proximal operator for g.
ts
Sequence of step sizes. use opt_ts() for stepsize generation
Value
The function returns the optimal solution
tathagatabasu/bootlasso documentation built on Aug. 9, 2019, 1:07 a.m.
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Description Usage Arguments Value
Description
Function for proximal-gradient optimization for non-differentiable functions We're trying to minimize f + g; where f is the differentiable part and g is the non-differentiable part.
Usage
1 |
Arguments
x |
Starting value. |
f |
Function to optimize. |
df |
Gradient of f. |
pg |
Proximal operator for g. |
ts |
Sequence of step sizes. use opt_ts() for stepsize generation |
Value
The function returns the optimal solution
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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