# [R] Optimization function producing negative parameter values

J C Nash pro|jcn@@h @end|ng |rom gm@||@com
Sun Mar 21 18:58:03 CET 2021

```This is likely because Hessian is being approximated.

Numerical approximation to Hessian will overstep the bounds because
the routines that are called don't respect the bounds (they likely
don't have the bounds available).

Writing numerical approximations that respect bounds and other constraints
is an interesting and very challenging problem. It's likely a lot easier to
do the work and get the derivatives analytically.

JN

On 2021-03-21 1:50 p.m., Bill Dunlap wrote:
> Does optim go out of bounds when you specify hessian=FALSE?
> hessian=TRUE causes some out-of-bounds evaluations of f.
>
>> optim(c(X=1,Y=1), function(XY){print(unname(XY));(XY[["X"]]+1)^4+(XY[["Y"]]-2)^4}, method= "L-BFGS-B", lower=c(0.001,0.001), upper=c(1.5,1.5), hessian=TRUE)
> [1] 1 1
> [1] 1.001 1.000
> [1] 0.999 1.000
> [1] 1.000 1.001
> [1] 1.000 0.999
> [1] 0.001 1.500
> [1] 0.002 1.500
> [1] 0.001 1.500
> [1] 0.001 1.500
> [1] 0.001 1.499
> [1] 0.003 1.500
> [1] 0.001 1.500
> [1] 0.002 1.501
> [1] 0.002 1.499
> [1] 0.001 1.500
> [1] -0.001  1.500
> [1] 0.000 1.501
> [1] 0.000 1.499
> [1] 0.002 1.501
> [1] 0.000 1.501
> [1] 0.001 1.502
> [1] 0.001 1.500
> [1] 0.002 1.499
> [1] 0.000 1.499
> [1] 0.001 1.500
> [1] 0.001 1.498
> \$par
>     X     Y
> 0.001 1.500
>
> \$value
> [1] 1.066506
>
> On Sun, Mar 21, 2021 at 10:22 AM Shah Alam <dr.alamsolangi using gmail.com> wrote:
>>
>> Dear all,
>>
>> I am using optim() to estimate unknown parameters by minimizing the
>> residual sums of squares. I created a function with the model. The model is
>> working fine. The optim function is producing negative parameter values, even
>> I have introduced upper and lower bounds (given in code). Therefore,
>> the model produces *NAs*.
>>
>> Following is my code.
>>
>> param <<- c(0.002,0.002, 0.14,0.012,0.01,0.02, 0.03, 0.001)# initial
>>> parameter values
>>> opt <- optim(param, fn= f.opt, obsdata =obsdata_10000, method= "L-BFGS-B",
>>> lower = c(0.001, 0.001, 0.08,0.008, 0.009, 0.008, 0.009, 0.001),
>>
>> upper = c(0.00375, 0.002, 0.2, 0.018, 0.08, 0.08, 0.08, 0.01),
>>> control=list(maxit=10), hessian = T)
>>
>>
>> Error:
>>
>> *"NAs producedError in if (rnd_1 < liferisk) { : missing value where
>> TRUE/FALSE needed "*
>>
>> The model function which produces NA due to negative parameter values
>>
>> liferisk <- rnorm(n = 1, mean =
>> (calib_para[which(names(calib_para)=="r_mu")]),sd =
>> (calib_para[which(names(calib_para)=="r_sd")]))
>>
>>   rnd_1 <- runif(1, 0, 1)
>>
>>   if (rnd_1 < liferisk) { ca_case <- 1} else {ca_case <- 0}
>>
>>
>> How to design/ modify optim() function, and upper-lower bounds to stop
>> producing negative values during parameter search?
>> Thanks
>>
>> Best regards,
>> Shah
>>
>>         [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help