msgl (version 2.3.9)

msgl.algorithm.config: Create a new algorithm configuration

Description

With the exception of verbose it is not recommended to change any of the default values.

Usage

msgl.algorithm.config(tolerance_penalized_main_equation_loop = 1e-10,
  tolerance_penalized_inner_loop_alpha = 1e-04,
  tolerance_penalized_inner_loop_beta = 1,
  tolerance_penalized_middel_loop_alpha = 0.01,
  tolerance_penalized_outer_loop_alpha = 0.01,
  tolerance_penalized_outer_loop_beta = 0,
  tolerance_penalized_outer_loop_gamma = 1e-05,
  use_bound_optimization = TRUE,
  use_stepsize_optimization_in_penalizeed_loop = TRUE,
  stepsize_opt_penalized_initial_t = 1, stepsize_opt_penalized_a = 0.1,
  stepsize_opt_penalized_b = 0.1, max_iterations_outer = 1e+05,
  inner_loop_convergence_limit = 1e+05, verbose = TRUE)

Arguments

tolerance_penalized_main_equation_loop

tolerance threshold.

tolerance_penalized_inner_loop_alpha

tolerance threshold.

tolerance_penalized_inner_loop_beta

tolerance threshold.

tolerance_penalized_middel_loop_alpha

tolerance threshold.

tolerance_penalized_outer_loop_alpha

tolerance threshold.

tolerance_penalized_outer_loop_beta

tolerance threshold.

tolerance_penalized_outer_loop_gamma

tolerance threshold.

use_bound_optimization

if TRUE hessian bound check will be used.

use_stepsize_optimization_in_penalizeed_loop

if TRUE step-size optimization will be used.

stepsize_opt_penalized_initial_t

initial step-size.

stepsize_opt_penalized_a

step-size optimization parameter.

stepsize_opt_penalized_b

step-size optimization parameter.

max_iterations_outer

max iteration of outer loop

inner_loop_convergence_limit

inner loop convergence limit.

verbose

If TRUE some information, regarding the status of the algorithm, will be printed in the R terminal.

Value

A configuration.

Examples

# NOT RUN {
data(SimData)

# A quick look at the data
dim(x)
table(classes)

# Create configuration
config <- msgl.algorithm.config(verbose = FALSE)

lambda <- msgl::lambda(x, classes, alpha = .5, d = 50,
 lambda.min = 0.05, algorithm.config = config)

fit <- msgl::fit(x, classes, alpha = .5, lambda = lambda,
 algorithm.config = config)

# }