mize_step_summary: Mize Step Summary
In jlmelville/mizer: Unconstrained Numerical Optimization Algorithms

Description Usage Arguments Details Value Examples

View source: R/mize.R

Produces a result summary for an optimization iteration. Information such as function value, gradient norm and step size may be returned.

1	mize_step_summary(opt, par, fg, par_old = NULL, calc_fn = NULL)

`opt`	Optimizer to generate summary for, from return value of `mize_step`.
`par`	Vector of parameters at the end of the iteration, from return value of `mize_step`.
`fg`	Function and gradient list. See the documentation of `mize`.
`par_old`	(Optional). Vector of parameters at the end of the previous iteration. Used to calculate step size.
`calc_fn`	(Optional). If `TRUE`, force calculation of function if not already cached in `opt`, even if it would not be needed for convergence checking.

By default, convergence tolerance parameters will be used to determine what function and gradient data is returned. The function value will be returned if it was already calculated and cached in the optimization iteration. Otherwise, it will be calculated only if a non-null absolute or relative tolerance value was asked for. A gradient norm will be returned only if a non-null gradient tolerance was specified, even if the gradient is available.

Note that if a function tolerance was specified, but was not calculated for the relevant value of par, they will be calculated here and the calculation does contribute to the total function count (and will be cached for potential use in the next iteration). The same applies for gradient tolerances and gradient calculation. Function and gradient calculation can also be forced here by setting the calc_fn and calc_gr (respectively) parameters to TRUE.

A list with the following items:

opt Optimizer with updated state (e.g. function and gradient counts).
iter Iteration number.
f Function value at par.
g2n 2-norm of the gradient at par.
ginfn Infinity-norm of the gradient at par.
nf Number of function evaluations so far.
ng Number of gradient evaluations so far.
step Size of the step between par_old and par, if par_old is provided.
alpha Step length of the gradient descent part of the step.
mu Momentum coefficient for this iteration

rb_fg <- list(
  fn = function(x) {
    100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2
  },
  gr = function(x) {
    c(
      -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]),
      200 * (x[2] - x[1] * x[1])
    )
  }
)
rb0 <- c(-1.2, 1)

opt <- make_mize(method = "BFGS", par = rb0, fg = rb_fg, max_iter = 30)
mize_res <- mize_step(opt = opt, par = rb0, fg = rb_fg)
# Get info about first step, use rb0 to compare new par with initial value
step_info <- mize_step_summary(mize_res$opt, mize_res$par, rb_fg, rb0)