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Duncan Murdoch

2024-Dec-13 18:45 UTC

[R] Non linear optimization with nloptr package fail to produce true optimal result

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

Daniel Lobo

2024-Dec-13 18:55 UTC

head link

[R] Non linear optimization with nloptr package fail to produce true optimal result

Hi Duncan,

I take your advice.

I posted here in search for a better answer to my problem as I could
not get that there.

My question is:
1. Why nloptr() is failing where other programs can continue with the
same set of data, numbers, and constraints?
2. Is this enough ground to say that nloptr is inferior and user
should not use this in complex problems?

I wish to get a thoughtful answer to above as my working environment
only has the nloptr package installed, and it is an isolated system
due to security issues and installation of a new package requires lot
lot of approvals and time consuming.

BTW, if someone interested here is my original post
https://stackoverflow.com/a/79271318/15910619

On Sat, 14 Dec 2024 at 00:15, Duncan Murdoch <murdoch.duncan at gmail.com>
wrote:>
> 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
>

J C Nash

2024-Dec-13 19:30 UTC

head link

[R] Non linear optimization with nloptr package fail to produce true optimal result

The following may or may not be relevant, but definitely getting somewhat
different results.
As this was a quick and dirty try while having a snack, it may have bugs.

# Lobo2412.R  -- from R Help 20241213

#Original artificial data

library(optimx)
library(nloptr)
library(alabama)

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))
flobo <- function(theta) {
sum(abs(X %*% theta - y))
}

#Constraint

eps <- 1e-4

hinlobo <- function(theta) {
   abs(sum(X %*% theta) - sum(y)) - 1e-3 + eps # ?? weird! (1e-4 - 1e-3)
}

Hxlobo <- function(theta) {
   X[100, , drop = FALSE] %*% theta - (120 - eps) # ditto -- also constant
}

conobj<-function(tt){
    ob <- flobo(tt)
    ci <- hinlobo(tt)
    if (ci > 0) {ci <- 0}
    ce <- Hxlobo(tt)
    si<-1; se<-1
    val<-ob+si*ci^2+se*ce^2
    cat("f, ci, ce,ob,val:"," ",ci," ",ce,"
",ob," ",val," at "); print(tt)
    val
}

t0<-rep(0,4)
conobj(t0)
t1 <- c(2.02, 6.764, 6.186, -20.095)
conobj(t1)
t2 <- c( -0.2186159, -0.5032066,  6.4458823, -0.4125948)
conobj(t2)


solo<-optimr(t0, conobj, gr="grcentral", method="anms",
control=list(trace=1))
solo
conobj(solo$par)
#Optimization with nloptr

# Sol = nloptr::auglag(t0, flobo, eval_g_ineq = hinlobo, eval_g_eq = Hxlobo,
opts # list("algorithm" = "NLOPT_LN_COBYLA",
"xtol_rel" = 1.0e-8, print_level=1))
# -0.2186159 -0.5032066  6.4458823 -0.4125948

sol <- auglag(par=t0, fn=flobo, hin=hinlobo, heq=Hxlobo,
control.outer=list(trace=TRUE))
sol

#=================================
J Nash

On 2024-12-13 13:45, Duncan Murdoch wrote:> 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.

John Fox

2024-Dec-13 19:44 UTC

head link

[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.

R help - Dec 2024 - Non linear optimization with nloptr package fail to produce true optimal result

[R] Non linear optimization with nloptr package fail to produce true optimal result

[R] Non linear optimization with nloptr package fail to produce true optimal result

[R] Non linear optimization with nloptr package fail to produce true optimal result

[R] Non linear optimization with nloptr package fail to produce true optimal result