[R] optim evils

[Rolf Turner]

I don't think anyone can do much to help you unless you show us (a) your
objective function "OF" and your starting value for "pars" --- which I 
do not
see in your posting.  Examples should be ***reproducible***!!!

My personal experience with optim() has always been very good.

     cheers,

     Rolf Turner

On 09/04/13 01:08, Michael Meyer wrote:
> Greetings,
>
> I am in great anguish as the routine stats::optim shows unexplicable behaviour
> of various sorts.
> For one it is immune to the choice of optimization method and seems to always do the same.
> The following trace log
>
>
> N = 21, M = 5 machine precision = 2.22045e-16
> At X0, 0 variables are exactly at the bounds
> At iterate     0  f=       1756.8  |proj g|=      0.73581
> At iterate     1  f =       911.52  |proj g|=       0.70136
> At iterate     2  f =       791.62  |proj g|=       0.68563
> At iterate     3  f =       749.81  |proj g|=             1
>      .....
>      .....
> At iterate    87  f =       666.91  |proj g|=       0.98217
> At iterate    88  f =        666.9  |proj g|=       0.96966
>
> Bad direction in the line search;
>     refresh the lbfgs memory and restart the iteration.
> At iterate    89  f =       9022.8  |proj g|=        1.0426
> iterations 89
> function evaluations 132
> segments explored during Cauchy searches 128
> BFGS updates skipped 0
> active bounds at final generalized Cauchy point 18
> norm of the final projected gradient 1.04257
> final function value 9022.84
> F = 9022.84
> final  value 9022.836050
> converged
>
>
> by each of the following calls to optim:
>
> optPars <- optim(   pars,OF,#gradientOF,
>                                  method  = "CG",
>                                  lower   = pars_lb,upper=pars_ub,
>                                  control = list(fnscale=1,trace=3,REPORT=1)
> )
> optPars <- optim(   pars,OF,#gradientOF,
>                                  method  = "Nelder-Mead",
>                                  lower   = pars_lb,upper=pars_ub,
>                                  control = list(fnscale=1,trace=3,REPORT=1)
> )
> optPars <- optim(   pars,OF,#gradientOF,
>                                  method  = "L-BFGS-B",
>                                  lower   = pars_lb,upper=pars_ub,
>                                  control = list(fnscale=1,trace=3,REPORT=1)
> )
>
> If method != "L-BFGS-B", then the routine complains about the uses of bounds for
> the parameters as expected, however the trace log above reminas the same.
>
> Note also that the routine makes fine progress toward a minimum (as desired)
> but in the last iteration reverses course and returns a function value much larger than the starting value.
>
> What is going on here?
> All help is much appreciated.