TA.info: Threshold-Accepting Information

In enricoschumann/NMOF: Numerical Methods and Optimization in Finance

Threshold-Accepting Information

Description

The function can be called from the objective and neighbourhood function during a run of TAopt; it provides information such as the current iteration, the current solution, etc.

Usage

  TA.info(n = 0L)

Arguments

n	generational offset; see Details.

Details

This function is still experimental.

The function can be called in the neighbourhood function or the objective function during a run of TAopt. It evaluates to a list with the state of the optimisation run, such as the current iteration.

TA.info relies on parent.frame to retrieve its information. If the function is called within another function in the neighbourhood or objective function, the argument n needs to be increased.

Value

A list

OF.sampling	logical: if TRUE, is the algorithm sampling the objective function to compute thresholds; otherwise (i.e. during the actual optimisation) FALSE
iteration	current iteration
step	current step (i.e. for a given threshold)
threshold	current threshold (the number, not the value)
xbest	the best solution found so far
OF.xbest	objective function value of best solution

Author(s)

Enrico Schumann

References

Gilli, M., Maringer, D. and Schumann, E. (2019) Numerical Methods and Optimization in Finance. 2nd edition. Elsevier. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/C2017-0-01621-X")}

Schumann, E. (2023) Financial Optimisation with R (NMOF Manual). http://enricoschumann.net/NMOF.htm#NMOFmanual

See Also

TAopt

Examples

### MINIMAL EXAMPLE for TAopt

## objective function evaluates to a constant
fun <- function(x)
    0

## neighbourhood function does not even change the solution,
## but it reports information
nb <- function(x) {
    tmp <- TA.info()
    cat("current threshold ",   tmp$threshold,
        "| current step ",      tmp$step,
        "| current iteration ", tmp$iteration, "\n")
    x
}

## run TA
algo <- list(nS = 5,
             nT = 2,
             nD = 3,
             x0 = rep(0, 5),
             neighbour = nb,
             printBar = FALSE,
             printDetail = FALSE)
ignore <- TAopt(fun, algo)

## printed output:
##    current threshold  NA | current step  1 | current iteration  NA
##    current threshold  NA | current step  2 | current iteration  NA
##    current threshold  NA | current step  3 | current iteration  NA
##    current threshold   1 | current step  1 | current iteration  1
##    current threshold   1 | current step  2 | current iteration  2
##    current threshold   1 | current step  3 | current iteration  3
##    current threshold   1 | current step  4 | current iteration  4
##    current threshold   1 | current step  5 | current iteration  5
##    current threshold   2 | current step  1 | current iteration  6
##    current threshold   2 | current step  2 | current iteration  7
##    current threshold   2 | current step  3 | current iteration  8
##    current threshold   2 | current step  4 | current iteration  9
##    current threshold   2 | current step  5 | current iteration  10

enricoschumann/NMOF documentation built on April 13, 2024, 12:16 p.m.