John Fox
2024-Dec-13 19:44 UTC
[R] Non linear optimization with nloptr package fail to produce true optimal result
Dear Daniel et al.,
Following on Duncan's remark and examining the message produced by
nloptr(), I simply tried increasing the maximum number of function
evaluations:
------ snip -------
> nloptr(rep(0, 4), f, eval_g_ineq = hin, eval_g_eq = Hx, opts +
list("algorithm" = "NLOPT_LN_COBYLA", "xtol_rel" =
1.0e-8,
+ maxeval = 1e5)
+ )
Call:
nloptr(x0 = rep(0, 4), eval_f = f, eval_g_ineq = hin, eval_g_eq = Hx,
opts = list(algorithm = "NLOPT_LN_COBYLA", xtol_rel = 1e-08,
maxeval = 1e+05))
Minimization using NLopt version 2.7.1
NLopt solver status: 4 ( NLOPT_XTOL_REACHED: Optimization stopped
because xtol_rel or xtol_abs (above) was reached. )
Number of Iterations....: 46317
Termination conditions: xtol_rel: 1e-08 maxeval: 1e+05
Number of inequality constraints: 1
Number of equality constraints: 1
Optimal value of objective function: 1287.71725107671
Optimal value of controls: 1.576708 6.456606 6.195305 -19.008
---------- snip ----------
That produces a solution closer to, and better than, the one that you
suggested (which you obtained how?):
> f(c(0.222, 6.999, 6.17, -19.371))
[1] 1325.076
I hope this helps,
John
--
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
web: https://www.john-fox.ca/
--
On 2024-12-13 1:45 p.m., Duncan Murdoch wrote:> Caution: External email.
>
>
> You posted a version of this question on StackOverflow, and were given
> advice there that you ignored.
>
> nloptr() clearly indicates that it is quitting without reaching an
> optimum, but you are hiding that message.? Don't do that.
>
> Duncan Murdoch
>
> On 2024-12-13 12:52 p.m., Daniel Lobo wrote:
>> library(nloptr)
>>
>> set.seed(1)
>> A <- 1.34
>> B <- 0.5673
>> C <- 6.356
>> D <- -1.234
>> x <- seq(0.5, 20, length.out = 500)
>> y <- A + B * x + C * x^2 + D * log(x) + runif(500, 0, 3)
>>
>> #Objective function
>>
>> X <- cbind(1, x, x^2, log(x))
>> f <- function(theta) {
>> sum(abs(X %*% theta - y))
>> }
>>
>> #Constraint
>>
>> eps <- 1e-4
>>
>> hin <- function(theta) {
>> ?? abs(sum(X %*% theta) - sum(y)) - 1e-3 + eps
>> }
>>
>> Hx <- function(theta) {
>> ?? X[100, , drop = FALSE] %*% theta - (120 - eps)
>> }
>>
>> #Optimization with nloptr
>>
>> Sol = nloptr(rep(0, 4), f, eval_g_ineq = hin, eval_g_eq = Hx, opts
>> list("algorithm" = "NLOPT_LN_COBYLA",
"xtol_rel" = 1.0e-8))$solution
>> # -0.2186159 -0.5032066? 6.4458823 -0.4125948
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide https://www.R-project.org/posting-
> guide.html
> and provide commented, minimal, self-contained, reproducible code.
J C Nash
2024-Dec-13 19:51 UTC
[R] Non linear optimization with nloptr package fail to produce true optimal result
Interesting that alabama and nloptr both use auglag but alabama gets a lower
objective fn.
I think there could be lots of exploration of controls and settings to play with
to find out
what is going on.
alabama::auglag
f, ci, ce,ob,val: 0 -4.71486e-08 1029.77 1029.77 at [1] -0.610594
4.307408 6.254267 -11.919881
> solfox<-c( 1.576708, 6.456606, 6.195305, -19.008 )
> conobj(solfox)
f, ci, ce,ob,val: 0 -1.031085e-05 1287.707 1287.707 at [1] 1.576708
6.456606 6.195305 -19.008000
[,1]
[1,] 1287.707
ci is the inequality constraint fn, ce the equality one, ob is the raw
objective, val the penalized one, then the 4
parameters.
JN
On 2024-12-13 14:44, John Fox wrote:> Dear Daniel et al.,
>
> Following on Duncan's remark and examining the message produced by
nloptr(), I simply tried increasing the maximum
> number of function evaluations:
> ------ snip -------
>
> > nloptr(rep(0, 4), f, eval_g_ineq = hin, eval_g_eq = Hx, opts >
+????????? list("algorithm" = "NLOPT_LN_COBYLA",
"xtol_rel" = 1.0e-8,
> +?????????????? maxeval = 1e5)
> + )
>
> Call:
>
> nloptr(x0 = rep(0, 4), eval_f = f, eval_g_ineq = hin, eval_g_eq = Hx,
> ??? opts = list(algorithm = "NLOPT_LN_COBYLA", xtol_rel = 1e-08,
> ??????? maxeval = 1e+05))
>
>
> Minimization using NLopt version 2.7.1
>
> NLopt solver status: 4 ( NLOPT_XTOL_REACHED: Optimization stopped
> because xtol_rel or xtol_abs (above) was reached. )
>
> Number of Iterations....: 46317
> Termination conditions:? xtol_rel: 1e-08??? maxeval: 1e+05
> Number of inequality constraints:? 1
> Number of equality constraints:??? 1
> Optimal value of objective function:? 1287.71725107671
> Optimal value of controls: 1.576708 6.456606 6.195305 -19.008
>
> ---------- snip ----------
>
> That produces a solution closer to, and better than, the one that you
suggested (which you obtained how?):
>
> > f(c(0.222, 6.999, 6.17, -19.371))
> [1] 1325.076
>
> I hope this helps,
> ?John
Daniel Lobo
2024-Dec-13 19:51 UTC
[R] Non linear optimization with nloptr package fail to produce true optimal result
Looks like the solution 1.576708 6.456606 6.195305 -19.007996 is the best solution that nloptr can produce by increasing the iteration numbers. The better set of solution is obtained using pracma package. On Sat, 14 Dec 2024 at 01:14, John Fox <jfox at mcmaster.ca> wrote:> > Dear Daniel et al., > > Following on Duncan's remark and examining the message produced by > nloptr(), I simply tried increasing the maximum number of function > evaluations: > ------ snip ------- > > > nloptr(rep(0, 4), f, eval_g_ineq = hin, eval_g_eq = Hx, opts > + list("algorithm" = "NLOPT_LN_COBYLA", "xtol_rel" = 1.0e-8, > + maxeval = 1e5) > + ) > > Call: > > nloptr(x0 = rep(0, 4), eval_f = f, eval_g_ineq = hin, eval_g_eq = Hx, > opts = list(algorithm = "NLOPT_LN_COBYLA", xtol_rel = 1e-08, > maxeval = 1e+05)) > > > Minimization using NLopt version 2.7.1 > > NLopt solver status: 4 ( NLOPT_XTOL_REACHED: Optimization stopped > because xtol_rel or xtol_abs (above) was reached. ) > > Number of Iterations....: 46317 > Termination conditions: xtol_rel: 1e-08 maxeval: 1e+05 > Number of inequality constraints: 1 > Number of equality constraints: 1 > Optimal value of objective function: 1287.71725107671 > Optimal value of controls: 1.576708 6.456606 6.195305 -19.008 > > ---------- snip ---------- > > That produces a solution closer to, and better than, the one that you > suggested (which you obtained how?): > > > f(c(0.222, 6.999, 6.17, -19.371)) > [1] 1325.076 > > I hope this helps, > John > -- > John Fox, Professor Emeritus > McMaster University > Hamilton, Ontario, Canada > web: https://www.john-fox.ca/ > -- > On 2024-12-13 1:45 p.m., Duncan Murdoch wrote: > > Caution: External email. > > > > > > You posted a version of this question on StackOverflow, and were given > > advice there that you ignored. > > > > nloptr() clearly indicates that it is quitting without reaching an > > optimum, but you are hiding that message. Don't do that. > > > > Duncan Murdoch > > > > On 2024-12-13 12:52 p.m., Daniel Lobo wrote: > >> library(nloptr) > >> > >> set.seed(1) > >> A <- 1.34 > >> B <- 0.5673 > >> C <- 6.356 > >> D <- -1.234 > >> x <- seq(0.5, 20, length.out = 500) > >> y <- A + B * x + C * x^2 + D * log(x) + runif(500, 0, 3) > >> > >> #Objective function > >> > >> X <- cbind(1, x, x^2, log(x)) > >> f <- function(theta) { > >> sum(abs(X %*% theta - y)) > >> } > >> > >> #Constraint > >> > >> eps <- 1e-4 > >> > >> hin <- function(theta) { > >> abs(sum(X %*% theta) - sum(y)) - 1e-3 + eps > >> } > >> > >> Hx <- function(theta) { > >> X[100, , drop = FALSE] %*% theta - (120 - eps) > >> } > >> > >> #Optimization with nloptr > >> > >> Sol = nloptr(rep(0, 4), f, eval_g_ineq = hin, eval_g_eq = Hx, opts > >> list("algorithm" = "NLOPT_LN_COBYLA", "xtol_rel" = 1.0e-8))$solution > >> # -0.2186159 -0.5032066 6.4458823 -0.4125948 > > > > ______________________________________________ > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide https://www.R-project.org/posting- > > guide.html > > and provide commented, minimal, self-contained, reproducible code. > >