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optimflex

optimflex provides a highly flexible suite of derivative-based non-linear optimization algorithms. It is specifically designed for researchers who require rigorous convergence control, particularly in complex models like SEM.

Why Use optimflex?

Standard optimization functions often rely on a single, fixed stopping rule. optimflex offers:

  1. Strict Convergence Control: Choose from 8 distinct criteria.
  2. The “AND” Rule: All selected criteria must be met simultaneously.
  3. Hessian Verification: Verify local minima by checking positive definiteness at the final point.

Installation

# install.packages("devtools")
# devtools::install_github("yourusername/optimflex")

Basic Usage

library(optimflex)

rosenbrock <- function(x) {
  100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2
}

res <- double_dogleg(
  start = c(-1.2, 1.0),
  objective = rosenbrock,
  control = list(
    use_grad = TRUE,
    use_rel_x = TRUE,
    use_posdef = TRUE
  )
)

print(res$par)
#> [1] 0.9999955 0.9999910

Convergence Criteria Overview

Flag Description
use_abs_f Absolute function change
use_rel_f Relative function change
use_abs_x Absolute parameter change
use_rel_x Relative parameter change
use_grad Gradient infinity norm
use_posdef Hessian Verification
use_pred_f Predicted Decrease

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.