# Perform axial search around a supposed MINIMUM and provide diagnostics Nonlinear optimization problems often terminate at points in the parameter space that are not satisfactory optima. This routine conducts an axial search, stepping forward and backward along each parameter and computing the objective function. This allows us to compute the `tilt` and `radius of curvature` or `roc` along that parameter axis. `axsearch` assumes that one is MINIMIZING the function `fn`. If you are working with a maximization, it is suggested that you write your own function that is to be minimized, that is, (-1)*(function to be maximized). All discussion here is in terms of minimization. Axial search may find parameters with a function value lower than that at the supposed minimum, i.e., lower than `fmin`. In this case `axsearch` exits immediately with the new function value and parameters. This can be used to restart an optimizer, as in the optimx wrapper. UTF-8 ```r axsearch(par, fn=NULL, fmin=NULL, lower=NULL, upper=NULL, bdmsk=NULL, control=list(), ...) ``` ## Arguments - `par`: A numeric vector of values of the optimization function parameters that are at a supposed minimum. - `fn`: The user objective function - `fmin`: The presumed value of the objective function at the parameters `par`. NOTE: This is NOT cheched. Caution!. However, if fmin==NULL on call, axsearch will compute the value. - `lower`: A vector of lower bounds on the parameters. - `upper`: A vector of upper bounds on the parameters. - `bdmsk`: An indicator vector, having 1 for each parameter that is "free" or unconstrained, and 0 for any parameter that is fixed or MASKED for the duration of the optimization. Partly for historical reasons, we use the same array during the progress of optimization as an indicator that a parameter is at a lower bound (bdmsk element set to -3) or upper bound (-1). - `control`: Algorithm controls as per `ctrldefault`. See `details`. - `...`: Extra arguments for the user function. ## Details The axial search MAY give a lower function value, in which case, one can restart an optimization. However, it is left to the user to do this. Its primary use is in presenting some features of the function surface in the tilt and radius of curvature measures returned. However, better measures should be possible, and this function should be regarded as largely experimental. Note: As of December 2021, the calling syntax has changed from `axsearch(par, fn=NULL, fmin=NULL, lower=NULL, upper=NULL, bdmsk=NULL,trace=0, ...)` In case any user has code employing the older function, it is to be found in `inst/doc/replaced2021/axsearch2018.R`. The new syntax has `trace` replaced with `control=list{}`, where the defaults are found from the function `ctrldefault()`. This routine uses three particular elements: `trace` is 0 if no intermediate output is desired, non-zero otherwise. `bigval` is a large number used to provide a value for the objective function when the parameters are inadmissible. `reltest` is used to test for equality of small numbers by comparing their sums with `reltest`. `grtesttol` is a small quantity, but it is used when multiplied by `reltest` to give `epst`, the axial step control. Each parameter is stepped by an amount `epst*(abs(parameter_value)+epst)`. Note that the author has never found it necessary to adjust these values from the defaults generated by `ctrldefault()`. ## Returns A list with components: - **bestfn**: The lowest (best) function value found during the axial search, else the original fmin value. (This is actively set in that case.) - **par**: The vector of parameters at the best function value. - **details**: A data frame reporting the original parameters, the forward step and backward step function values, the size of the step taken for a particular parameter, the tilt and the roc (radius of curvature). Some elements will be NA if we find a lower function value during the axial search. ## Examples ```r ##################### # require(optimx) # Simple bounds test for n=4 bt.f<-function(x){ sum(x*x) } bt.g<-function(x){ gg<-2.0*x } n<-4 lower<-rep(0,n) upper<-lower # to get arrays set bdmsk<-rep(1,n) # bdmsk[(trunc(n/2)+1)]<-0 for (i in 1:n) { lower[i]<-1.0*(i-1)*(n-1)/n upper[i]<-1.0*i*(n+1)/n } xx<-0.5*(lower+upper) cat("lower bounds:") print(lower) cat("start: ") print(xx) cat("upper bounds:") print(upper) abtrvm <- list() # ensure we have the structure cat("Rvmmin \n\n") # Note: trace set to 0 below. Change as needed to view progress. # Following can be executed if package optimx available # abtrvm <- optimr(xx, bt.f, bt.g, lower=lower, upper=upper, method="Rvmmin", # control=list(trace=0)) # Note: use lower=lower etc. because there is a missing hess= argument # print(abtrvm) abtrvm$par <- c(0.00, 0.75, 1.50, 2.25) abtrvm$value <- 7.875 cat("Axial search") axabtrvm <- axsearch(abtrvm$par, fn=bt.f, fmin=abtrvm$value, lower, upper, bdmsk=NULL) print(axabtrvm) abtrvm1 <- optimr(xx, bt.f, bt.g, lower=lower, upper=upper, method="Rvmmin", control=list(maxit=1, trace=0)) proptimr(abtrvm1) abtrvm1$value <- 8.884958 abtrvm1$par <- c(0.625, 1.625, 2.625, 3.625) cat("Axial search") axabtrvm1 <- axsearch(abtrvm1$par, fn=bt.f, fmin=abtrvm1$value, lower, upper, bdmsk=NULL) print(axabtrvm1) cat("Do NOT try axsearch() with maximize\n") ```

axsearch function