% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tune_Lambda.R
\name{.tune_grid_search}
\alias{.tune_grid_search}
\title{Grid Search Initialization for Penalty Tuning}
\usage{
.tune_grid_search(
  log_initial_wiggle,
  log_initial_flat,
  log_penalty_vec,
  criterion_fxn,
  env,
  include_warnings,
  ...
)
}
\arguments{
\item{log_initial_wiggle}{Numeric vector; log-scale candidate values for
the wiggle penalty.}

\item{log_initial_flat}{Numeric vector; log-scale candidate values for
the flat ridge penalty.}

\item{log_penalty_vec}{Numeric vector; log-scale predictor/partition
penalties.}

\item{criterion_fxn}{Function; tuning-objective evaluation function.}

\item{env}{List; tuning environment.}

\item{include_warnings}{Logical; whether to print warnings on failure.}

\item{...}{Additional arguments passed to \code{criterion_fxn}.}
}
\value{
Numeric vector of length 2; the best (log_wiggle, log_flat) found.
}
\description{
Evaluates the selected tuning criterion over a grid of initial wiggle and ridge
penalty values to find a good starting point for BFGS optimization.
}
\keyword{internal}
