[Rd] nlminb with constraints failing on some platforms

[Brad Bell]

I get the failure message. To be specific:

adcomp.git>R CMD BATCH --quiet test_nlminb.R
adcomp.git>cat test_nlminb.Rout
 > f <- function(x) sum( log(diff(x)^2+.01) + (x[1]-1)^2 )
 > opt <- nlminb(rep(0, 10), f, lower=-1, upper=3)
 > xhat <- rep(1, 10)
 > abs( opt$objective - f(xhat) ) < 1e-4  ## Must be TRUE
[1] FALSE

My system is described by:
adcomp.git>uname -a
Linux localhost.localdomain 4.17.7-200.fc28.x86_64 #1 SMP Tue Jul 17 16:28:31 UTC 2018 x86_64 x86_64 
x86_64 GNU/Linux

My version of R is described by:
Source       : R-3.5.2-2.fc28.src.rpm

I have tried passing in the gradient and turning on the trace and it gives nearly the exact same 
trace with and without the gradient.
Here is the output of a very similar case with the gradient:

 > n    <- 10
 > f    <- function(x) {
+       result <- 0.0
+       for( i in 2 : n ) {
+               result <- result + log( (x[i] - x[i-1])^2 + 0.01 ) + (x[1] - 1.0)^2
+       }
+       result
+ }
 > g    <- function(x) {
+       result <- rep(0.0, n)
+       for( i in 2 : n ) {
+               result[1]   <- result[1] + 2.0 * (x[1] - 1.0)
+               log_arg     <- ( x[i] - x[i-1] )^2 + 0.01
+               log_arg_i   <- 2.0 * (x[i] - x[i-1])
+               result[i]   <- result[i]   + log_arg_i / log_arg
+               result[i-1] <- result[i-1] - log_arg_i / log_arg
+       }
+       result
+ }
 > xstart <- rep(0.0, n)
 > opt    <- nlminb(
+       xstart,
+       objective=f ,
+       gradient=g,
+       lower=-3,
+       upper=3,
+       control=list(trace=1)
+ )
   0:    -32.446532:  0.00000  0.00000  0.00000  0.00000  0.00000 0.00000  0.00000  0.00000  
0.00000  0.00000
... snip ...
150:    -37.750217: 0.796764 0.303221 0.285377 0.271175 0.257584 0.248540 0.239230 0.234184 0.229395 
0.227872
 > opt$par
  [1] 0.7967636 0.3032208 0.2853775 0.2711747 0.2575838 0.2485403 0.2392304
  [8] 0.2341841 0.2293946 0.2278722
 > g(opt$par)
  [1]  0.23427575 -0.43398577 -0.67415314 -0.11550223 -0.87486481 0.05194325
  [7] -0.83926642 -0.05100054 -0.65128392 -0.30441806
 >