Description Usage Arguments Details Value Examples
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

par 
Vector of parameters at the end of the iteration, from return value
of 
fg 
Function and gradient list. See the documentation of

par_old 
(Optional). Vector of parameters at the end of the previous iteration. Used to calculate step size. 
calc_fn 
(Optional). If 
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 nonnull absolute or relative tolerance value was asked for. A gradient norm will be returned only if a nonnull 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
2norm of the gradient at par
.
ginfn
Infinitynorm 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  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)

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