Initialise the graduated optimisation with an "Ordinary Least Squares" solution

Initialise the graduated optimisation with an "Ordinary Least Squares" solution

Usage

slise_initialisation_ols(
  X,
  Y,
  epsilon,
  weight = NULL,
  beta_max = 20/epsilon^2,
  max_approx = 1.15,
  max_iterations = 300,
  beta_max_init = 2.5/epsilon^2,
  ...
)

Arguments

X

data matrix

Y

response vector

epsilon

error tolerance

weight

weight vector (default: NULL)

beta_max

the maximum starting sigmoid steepness (default: 20/epsilon^2)

max_approx

the target approximation ratio (default: 1.15)

max_iterations

if ncol(X) is huge, then ols is replaced with optimisation (default:300)

beta_max_init

the maximum sigmoid steepness in the initialisation

...

unused parameters

Value

list(alpha, beta)