lasso_task: LASSO logistic regression problem

In bhtang127/AccelBenchmark: Benchmark Various Accelerating Methods for Fixed Point Iterations

LASSO logistic regression problem

Description

A function that will generate the task list LASSO logistic regression problem.

Usage

lasso_task(lam)

Arguments

lam	Weight for the L1 penalty

Value

A list containing all components needed for benchmarking the problem

initfn	Parameter random initializing function
fixptfn	Updating function for the fixed point iteration problem
objfn	Function calculating the objective value for current parameter
...	Other arguments required in functions above

References

Guyon I, Gunn SR, Ben-Hur A, Dror G (2004). Result analysis of the NIPS 2003 feature selection challenge. In: NIPS, volume 4, 545–552.

Examples

## Not run: 
set.seed(54321)
problem = lasso_task(lam=1)
benchmark(
  problem,
  algorithm=c("raw", "squarem", "daarem", "pem", "qn", "nes"),
  ntimes = 200
)

## End(Not run)

bhtang127/AccelBenchmark documentation built on May 30, 2022, 2:21 a.m.