% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sigma_update.R
\name{update_sigma_cgt}
\alias{update_sigma_cgt}
\title{Update Regularization Parameter (Classical CGT)}
\usage{
update_sigma_cgt(
  sigma_current,
  rho,
  eta1 = 0.1,
  eta2 = 0.9,
  gamma1 = 0.5,
  gamma2 = 2,
  sigma_min = 1e-16,
  sigma_max = 1e+16
)
}
\arguments{
\item{sigma_current}{Current regularization parameter (positive scalar)}

\item{rho}{Ratio of actual to predicted reduction (scalar)}

\item{eta1}{Acceptance threshold (default: 0.1). Steps with rho >= eta1 are accepted.}

\item{eta2}{Very successful threshold (default: 0.9). Steps with rho >= eta2 trigger sigma decrease.}

\item{gamma1}{Sigma decrease factor for very successful steps (default: 0.5)}

\item{gamma2}{Sigma increase factor for unsuccessful steps (default: 2.0)}

\item{sigma_min}{Minimum allowed sigma (default: 1e-16)}

\item{sigma_max}{Maximum allowed sigma (default: 1e16)}
}
\value{
Updated sigma value (scalar)
}
\description{
Adaptively adjusts the regularization parameter sigma based on how well the
cubic model predicted the actual function decrease. Follows the classical
Cartis-Gould-Toint (2011) update strategy.
}
\details{
The update strategy is:
\itemize{
\item \strong{Very successful} (rho >= eta2): Decrease sigma by gamma1 factor (trust model more)
\item \strong{Moderately successful} (eta1 <= rho < eta2): Keep sigma unchanged
\item \strong{Unsuccessful} (rho < eta1): Increase sigma by gamma2 factor (trust model less)
}

The ratio rho measures model quality: rho = (f(x) - f(x+s)) / pred_reduction.
Values near 1 indicate excellent model accuracy; values near 0 or negative
indicate poor model fit.
}
\keyword{internal}
