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
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] 1.00
Can you put together your example as a single runnable scipt?
If so, I'll try some other tools to see what is going on. There
have been rumours of some glitches in the L-BFGS-B R implementation,
but so far I've not been able to acquire any that I can reproduce.
John Nash (maintainer of optimx pac
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). Therefor
This is highly problem dependent... and you appear to already know the answer.
Note that some differential evolution solution approaches may benefit from
parallelizing evaluation of generations since within that sub-problem the
optimization dependencies don't apply.
A theoretical discussion fo
More of a general query, but looking to see if others have successfully used
something like the foreach package (or other parallel style functions) with
certain functions that minimize likelihood or objective functions (e.g.,
optim/nlminb).
I have had great success with embarrassingly parallel
ent is zero because the objective function is flat.
This would normally be considered a "user error" but perhaps John's
newer code can catch it?
Paul
Date: Thu, 29 Sep 2016 14:53:08 -0500
From: Narendra Modi
To: "r-help@r-project.org"
Subject: [R] Optimizatio
I haven't tried running your code, but a quick read suggests you should
1) set up the input data so your code can be run with source() without any
preprocessing.
2) compute the function for several sets of parameters to make sure it is
correct. Maybe
create a very simple test case you can more o
I have put together a R snippet wherein I am trying to get optimum
values for which error is minimized. The error is the difference
between two matrices.
Every time I run the below code, I don't see any optimization
happening as in the final answer is the same as the initial estimate
regardless of
At the "solution" -- which nlm seems to find OK -- you have a very
nasty scaling issue. exp(z) has value > 10^300.
Better transform your problem somehow to avoid that. You are taking
log of this except for adding 1, so effectively have just z. But you
should look at it carefully and do a number of
hello all,
I am getting wrong estimates from this code. do you know what could be the
problem.
thanks
x<- c(1.6, 1.7, 1.7, 1.7, 1.8, 1.8, 1.8, 1.8)
y <- c( 6, 13, 18, 28, 52, 53, 61, 60)
n <- c(59, 60, 62, 56, 63, 59, 62, 60)
DF <- data.frame(x, y, n)
# note: there is no need to have the ch
Thanks to all who responded,
I've found a very useful code here:
http://courses.washington.edu/fish507/notes.html
In particular the Lecture 3...
Héctor
2015-10-17 7:05 GMT+00:00 Berend Hasselman :
>
> Your model is producing -Inf entries in the vector Be (in function modl
> and LL) at some s
Your model is producing -Inf entries in the vector Be (in function modl and LL)
at some stage during the optimization process.
You should first do something about that before anything else.
Berend
> On 17 Oct 2015, at 03:01, Bert Gunter wrote:
>
> I made no attempt to examine your details fo
I made no attempt to examine your details for problems, but in general,
My problem
> is that the results change a lot depending on the initial values... I can't
> see what I am doing wrong...
>
> This is a symptom of an overparameterized model: The parameter estimates
> are unstable even though t
Dear R users,
I'im trying to find the parameters of a dynamic biomass model using maximum
likelihood estimation. I used two approaches, one by hand, with optim()
function and the other using mle2() function from package bbmle. My problem
is that the results change a lot depending on the initial va
On Thu, 17 Sep 2015, "Patzelt, Edward" writes:
> R Help -
>
> I am trying to use a grid search for a 2 free parameter reinforcement
> learning model and the grid search is incredibly slow. I've used optimx but
> can't seem to get reasonable answers. Is there a way to speed up this grid
> search d
optimx does nothing to speed up optim or the other component optimizers.
In fact, it does a lot of checking and extra work to improve reliability
and add KKT tests that actually slow things down. The purpose of optimx
is to allow comparison of methods and discovery of improved approaches
to a p
R Help -
I am trying to use a grid search for a 2 free parameter reinforcement
learning model and the grid search is incredibly slow. I've used optimx but
can't seem to get reasonable answers. Is there a way to speed up this grid
search dramatically?
dat <- structure(list(choice = c(0, 1, 1, 1,
On Sat, Mar 21, 2015 at 3:41 PM, Prof Brian Ripley
wrote:
> On 21/03/2015 14:27, Johannes Radinger wrote:
>
>> Thanks for the fast response. The fitdistr() function works well for the
>> predefined density functions. However, what is the recommended approach
>> to optimize/fit a density function
On 21/03/2015 14:27, Johannes Radinger wrote:
Thanks for the fast response. The fitdistr() function works well for the
predefined density functions. However, what is the recommended approach
to optimize/fit a density function described by two superimposed normal
distributions? In my case it is N1
Thanks for the fast response. The fitdistr() function works well for the
predefined density functions. However, what is the recommended approach to
optimize/fit a density function described by two superimposed normal
distributions? In my case it is N1(mean=0,sd1)*p+N2(mean=0,sd2)*(1-p). With
fitdis
One way using the standard R distribution:
library(MASS)
?fitdistr
No optimization is needed to fit a normal distribution, though.
On 21/03/2015 13:05, Johannes Radinger wrote:
Hi,
I am looking for a way to fit data (vector of values) to a density function
using an optimization (ordinary leas
Hi,
I am looking for a way to fit data (vector of values) to a density function
using an optimization (ordinary least squares or maximum likelihood fit).
For example if I have a vector of 100 values generated with rnorm:
rnorm(n=100,mean=500,sd=50)
How can I fit these data to a Gaussian density
Hi,
I want to optimize the root mean square error objective function using the
optim function. Thus the function will look like sqrt(sum((yi - f(xi))^2)/n).
Now the f(xi) is the Arima function. I am not clear how do I get the f(xi)
because the call to arima function in C gives the value of th
There is an error jean, I apologize... I made changes to the vectors and did
not correct the bottom line... this is the correct run:a <-c(0,1,1,0,1,0,0,0,0)
b <-c(0,0,0,1,0,0,0,0,0)
c <-c(1,0,1,0,1,1,0,0,0)
d <-c(0,1,0,1,0,1,0,0,0)
df <-rbind(a,b,c,d)
df <-cbind(df,h=c(sum(a)*8,sum(b)*8,sum(c)*8,s
Andras,
Is there an error in your post or am I missing something?
df[, 9] is made up of the last (9th) element of each of a, b, c, and d.
The minimum value for sum(df[, 9]) is 0.
Given your conditions, there are many, many ways to get this result.
Here is just one example:
a <-c(1,1,1,1,1,0,0,0,0
Dear All,
please provide help with the following:
we have
a <-c(0,1,1,0,1,0,0,0,0)
b <-c(0,0,0,1,0,0,0,0,0)
c <-c(1,0,1,0,1,1,0,0,0)
d <-c(0,1,0,1,0,1,0,0,0)
df <-rbind(a,b,c,d)
df <-cbind(df,h=c(sum(a)*8,sum(b)*8,sum(c)*8,sum(d)*8))
df <-cbind(df,df[,8]*c(1,2,3,2))
I would like to minimize the
On 11/17/13 11:49, Dennis Murphy wrote:
There are lots of errors in your code. In particular, the optimization
routines do not like functions that ignore the parameters.
I would like to nominate this delicious riposte as a fortune
candidate. Anyone to second the motion?
Indeed. I so second!
> Please try some of the examples for optim or optimx to learn how to
> structure your problem.
>
> JN
>
>
> On 13-11-16 06:00 AM, r-help-requ...@r-project.org wrote:
>> Message: 19
>> Date: Fri, 15 Nov 2013 09:17:47 -0800 (PST)
>> From: IZHAK shabsogh
>> T
optimx to learn how to
structure your problem.
JN
On 13-11-16 06:00 AM, r-help-requ...@r-project.org wrote:
> Message: 19
> Date: Fri, 15 Nov 2013 09:17:47 -0800 (PST)
> From: IZHAK shabsogh
> To: "r-help@r-project.org"
> Subject: [R] optimization
> Message-ID:
>
x1<-c(5.548,4.896,1.964,3.586,3.824,3.111,3.607,3.557,2.989,18.053,3.773,1.253,2.094,2.726,1.758,5.011,2.455,0.913,0.890,2.468,4.168,4.810,34.319,1.531,1.481,2.239,4.204,3.463,1.727)
y<-c(2.590,3.770,1.270,1.445,3.290,0.930,1.600,1.250,3.450,1.096,1.745,1.060,0.890,2.755,1.515,4.770,2.220,0.590,0.5
Jean-Francois Chevalier bisnode.com> writes:
>
You have already given the answer yourself. You have binary variables x(j, i),
you need to set up the inequalities, and then apply one of the mixed-integer
linear programming solvers in R, for instance 'lpSolve', 'Rglpk', 'Rsymphony'.
Setting up th
It would be more clear if you tell, what you want to do instead of what you do
not want to do.
If you start with a usual cost matrix (whatever cost function you have) and you
have to assign N to N this reduces to the well-known Munkre’s algorithm (see
for example: http://gallery.rcpp.org/artic
Hello,
I'm trying to solve a multiple assignment problem.
I found a package Adagio and its function mknapsack which
maximize vstar = p(1)*(x(1,1) + ... + x(m,1)) + ... ... + p(n)*(x(1,n) + ... +
x(m,n))
subject to w(1)*x(i,1) + ... + w(n)*x(i,n) <= k(i) for i=1,...,m
x(1,j) + ... + x(m,j) <= 1
On 29 Oct 2013, at 21:35 , Rolf Turner wrote:
> On 10/29/13 19:44, peter dalgaard wrote:
>
>
>
>> There really is no substitute for knowledge and understanding! Did it not
>> occur to you that the Windspeed column needs to enter into your analysis?
>
>
>
> Fortune!
Actually, I felt
Which suggests the OP should verify that the data in "...$Frequency" is the
data he expects to be there.
Rui Barradas wrote
> Hello,
>
> I can't reproduce your error:
>
> windfreq <-
> c(1351L, 2147L, 3317L, 4378L, 5527L, 6667L, 7865L, 8970L, 9987L,
> 10907L, 11905L, 12642L, 131000L, 14983L, 1
On 10/29/13 19:44, peter dalgaard wrote:
There really is no substitute for knowledge and understanding! Did it not occur
to you that the Windspeed column needs to enter into your analysis?
Fortune!
cheers,
Rolf Turner
__
R-hel
On 28 Oct 2013, at 13:07 , kmmoon100 wrote:
> Hello everyone,
>
> This is Kangmin.
>
> I am trying to produce shape and scale of my wind data. My data is based on
> wind speed frequency with 1km/hr increment. data is described below.
>
> Windspeed (km/h)Frequency
> 1 351
> 2 147
>
On 29-10-2013, at 00:35, kmmoon100 wrote:
> Hi Berend,
>
> Thank you for your reply.
> How can I use dput function for this type of data?
> I looked up the description of the function but I still can't understand how
> to use it for solving my error.
>
You don't use dput() to solve your error
Hi Berend,
Thank you for your reply.
How can I use dput function for this type of data?
I looked up the description of the function but I still can't understand how
to use it for solving my error.
Regards,
Kangmin.
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On 28-10-2013, at 16:07, Rui Barradas wrote:
> Hello,
>
> I can't reproduce your error:
>
> windfreq <-
> c(1351L, 2147L, 3317L, 4378L, 5527L, 6667L, 7865L, 8970L, 9987L,
> 10907L, 11905L, 12642L, 131000L, 14983L, 15847L, 16842L, 17757L,
> 18698L, 19632L, 20626L, 21599L, 22529L, 23325L, 24391L
Hello,
I can't reproduce your error:
windfreq <-
c(1351L, 2147L, 3317L, 4378L, 5527L, 6667L, 7865L, 8970L, 9987L,
10907L, 11905L, 12642L, 131000L, 14983L, 15847L, 16842L, 17757L,
18698L, 19632L, 20626L, 21599L, 22529L, 23325L, 24391L, 25356L,
26267L, 27230L, 28223L, 29190L, 30142L, 31124L, 32104
Hello everyone,
This is Kangmin.
I am trying to produce shape and scale of my wind data. My data is based on
wind speed frequency with 1km/hr increment. data is described below.
Windspeed (km/h)Frequency
1 351
2 147
3 317
4 378
5 527
6 667
7 865
8 970
9 987
10 907
11 905
12 64
Hello everyone,
This is Kangmin.
I am trying to produce shape and scale of my wind data. My data is based on
wind speed frequency with 1km/hr increment. data is described below.
Windspeed (km/h)Frequency
1 351
2 147
3 317
4 378
5 527
6 667
7 865
8
Dear Graham,
On 16 June 2013 02:08, Graham McDannel wrote:
> I am attempting to optimize a function I have developed using optim.
>
> I am getting the below error message:
>
> Error in n < 1: 'n' is missing
>
I suspect a function requires an argument named n, and you
didn't pass one. Either in
The r-help list should institute a prize for "Most Obtuse Question
of the Month". This one should be a shoe-in for the June 2013 prize.
cheers,
Rolf Turner
On 16/06/13 12:08, Graham McDannel wrote:
I am attempting to optimize a function I have developed using optim.
I am gettin
Not unless you read the Posting Guide, stop posting in HTML mail format, and
provide a reproducible example.
---
Jeff NewmillerThe . . Go Live...
DCN:Basics: ##.#. ##.#.
I am attempting to optimize a function I have developed using optim.
I am getting the below error message:
Error in n < 1: 'n' is missing
Could some one provide some additional clarity regarding this message and
what it entails, as well as, how to rectify this issue.
Thanks
[[alternati
Hello,
You cannot change the numerical accuracy, it's a built-in constant. To
see it use
?.Machine
.Machine$double.eps # smallest value different from zero
Actually, .Machine$double.eps is the "the smallest positive
floating-point number x such that 1 + x != 1"
You can try the following
Rui, thanks for your reply. You meant that it is the issue of accuracy? So if
I change the numerical accuracy, my results can be output? Thanks a lot!
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Sent from the R help mailing list arc
Hello,
Your thoght is mathematically right but numerically wrong. The result
given by optimize is so close to the real minimum that numerical
accuracy comes in and it becomes indistinguishable from the value you're
expecting.
You get the minimum up to a certain accuracy, not more.
Hope this
Thank you professor. I think the minimum value of x^2 between -1 and 1 should
be x=0, y=0. but the result is not that. I am thinking is any wrong with my
thought?
Thanks for helping me out!
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On Apr 10, 2013, at 03:24 , nntx wrote:
> As a simple example, I want to find minimum value for x^2, but it can't be
> obtained by:
> f<-function(x)x^2
> optimize(f,lower=-1,upper=1)
Works fine for me. What did you expect it to do?
> f<-function(x)x^2
> optimize(f,lower=-1,upper=1)
$minimum
[1]
As a simple example, I want to find minimum value for x^2, but it can't be
obtained by:
f<-function(x)x^2
optimize(f,lower=-1,upper=1)
What are other methods to deal with this? I tried DEoptim, still doesn't
work. Any suggustions will be extremely helpful! THanks!
Shelly
--
View this message
you can see in an example.
> I used package lpsolve, but it does not work. I am not sure how to treat
> with this part of statement, I think I made mistake in it:
> row.rhs <- c(15,10,5,30) and
> col.rhs <- c(1,1,1,1,1,1)
>
> The example in R:
>
> library(lpSolve)
&g
Pavel_K vsb.cz> writes:
>
> Dear all,
> I am trying to find the solution for the optimization problem focused on
> the finding minimum cost.
> I used the solution proposed by excel solver, but there is a restriction
> in the number of variables.
>
> My data consists of 300 rows represent cities
am not sure how to treat
with this part of statement, I think I made mistake in it:
row.rhs <- c(15,10,5,30) and
col.rhs <- c(1,1,1,1,1,1)
The example in R:
library(lpSolve)
costs <- as.matrix(read.table("C:/R/OPTIMIZATION/DATA.TXT", dec = ",",
sep=";",
On 11-03-2013, at 23:31, Pavel_K wrote:
> Dear all,
> I am trying to find the solution for the optimization problem focused on the
> finding minimum cost.
> I used the solution proposed by excel solver, but there is a restriction in
> the number of variables.
>
> My data consists of 300 rows re
Dear all,
I am trying to find the solution for the optimization problem focused on the
finding minimum cost.
I used the solution proposed by excel solver, but there is a restriction in
the number of variables.
My data consists of 300 rows represent cities and 6 columns represent the
centres. It co
On 09-02-2013, at 21:08, Axel Urbiz wrote:
> Dear List,
>
> I'm new in R. I'm trying to solve a simple constrained optimization
> problem.
>
> Essentially, let's say I have a matrix as in the object 'mm' inside the
> function below. My objective function should have a matrix of parameters,
> o
Dear List,
I'm new in R. I'm trying to solve a simple constrained optimization
problem.
Essentially, let's say I have a matrix as in the object 'mm' inside the
function below. My objective function should have a matrix of parameters,
one parameter for each element 'mm' (4 in this case). The prob
On 26-10-2012, at 21:41, Richard James wrote:
>
> That solution works very well.
>
> The only issue is that 'rnorm' occasionally generates negative values which
> aren't logical in this situation.
>
Try another random generator.
Lognormal, uniform, ...
> Is there a way to set a lower limit
That solution works very well.
The only issue is that 'rnorm' occasionally generates negative values which
aren't logical in this situation.
Is there a way to set a lower limit of zero?
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On 26-10-2012, at 12:50, Richard James wrote:
> Dear Berend and Thomas,
>
> thank you for suggesting the lsei function. I found that the tlsce {BCE}
> function also works very well:
>
> library("BCE")
> tlsce(A=bmat,B=target)
>
> The limSolve package has an 'xsample' function for generating un
On 26-10-2012, at 12:50, Richard James wrote:
> Dear Berend and Thomas,
>
> thank you for suggesting the lsei function. I found that the tlsce {BCE}
> function also works very well:
>
> library("BCE")
> tlsce(A=bmat,B=target)
>
> The limSolve package has an 'xsample' function for generating un
Dear Berend and Thomas,
thank you for suggesting the lsei function. I found that the tlsce {BCE}
function also works very well:
library("BCE")
tlsce(A=bmat,B=target)
The limSolve package has an 'xsample' function for generating uncertainty
values via Monte-Carlo simulation, however it only works
Dear Berend,
Many thanks for taking your time to assist with this optimization problem.
I'll work on data this week and let you know how I get on.
Again, many thanks
Richard
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On 21-10-2012, at 13:37, Thomas Schu wrote:
> Dear Richard,
>
> It is funny. I have to perform the approach of sediment fingerprinting for
> my master thesis. Mr. Hasselman gave me the advice to take a closer look
> into the limSolve package a few days ago.
> http://cran.r-project.org/web/packa
Dear Richard,
It is funny. I have to perform the approach of sediment fingerprinting for
my master thesis. Mr. Hasselman gave me the advice to take a closer look
into the limSolve package a few days ago.
http://cran.r-project.org/web/packages/limSolve/index.html
I guess, the lsei-function of thi
I do not know what algorithms the Excel solver function uses.
See inline for how to do what you want in R.
Forgive me if I have misinterpreted your request.
On 19-10-2012, at 16:25, Richard James wrote:
> Dear Colleagues,
> I am attempting to develop an optimization routine for a river suspende
On Mon, Jul 30, 2012 at 06:51:47AM -0700, Megh Dal wrote:
> Hi, I have following optimization problem:
>
> Min: x1 + x2 +...+ x7
> subject to:
>
> x1 + x2 >= 80
> x2 + x3 >= 65
> x3 + x4 >= 40
>
> all xi are ***positive integer***.
>
> Can somebody help me in this optimization problem?
Hi.
As
Hi, I have following optimization problem:
Min: x1 + x2 +...+ x7
subject to:
x1 + x2 >= 80
x2 + x3 >= 65
x3 + x4 >= 40
all xi are ***positive integer***.
Can somebody help me in this optimization problem?
Thanks for your help
__
R-help@r-project.org
On Thu, May 17, 2012 at 06:14:37PM -0400, Nathan Stephens wrote:
> I have a very simple maximization problem where I'm solving for the vector
> x:
>
> objective function:
> w'x = value to maximize
>
> box constraints (for all elements of w):
> low < x < high
>
> equality constraint:
> sum(x) = 1
Marc Girondot yahoo.fr> writes:
>
> Le 18/05/12 00:14, Nathan Stephens a écrit :
> > I have a very simple maximization problem where I'm solving for the vector
> > But I get inconsistent results depending on what starting values I. I've
> > tried various packages but none seem to bee the very sol
On May 18, 2012, at 09:10 , Hans W Borchers wrote:
> peter dalgaard gmail.com> writes:
>>
>> On May 18, 2012, at 00:14 , Nathan Stephens wrote:
>>
>>> I have a very simple maximization problem where I'm solving for the vector
>>> x:
>>>
>>> objective function:
>>> w'x = value to maximize
>>>
Le 18/05/12 00:14, Nathan Stephens a écrit :
I have a very simple maximization problem where I'm solving for the vector
x:
objective function:
w'x = value to maximize
box constraints (for all elements of w):
low< x< high
equality constraint:
sum(x) = 1
But I get inconsistent results dependi
peter dalgaard gmail.com> writes:
>
> On May 18, 2012, at 00:14 , Nathan Stephens wrote:
>
> > I have a very simple maximization problem where I'm solving for the vector
> > x:
> >
> > objective function:
> > w'x = value to maximize
> >
> > box constraints (for all elements of w):
> > low < x
On May 18, 2012, at 00:14 , Nathan Stephens wrote:
> I have a very simple maximization problem where I'm solving for the vector
> x:
>
> objective function:
> w'x = value to maximize
>
> box constraints (for all elements of w):
> low < x < high
>
> equality constraint:
> sum(x) = 1
>
> But I
I have a very simple maximization problem where I'm solving for the vector
x:
objective function:
w'x = value to maximize
box constraints (for all elements of w):
low < x < high
equality constraint:
sum(x) = 1
But I get inconsistent results depending on what starting values I. I've
tried variou
Hi Greg,
The problem is that I also have restrictions for each variable (they must be
higher than -.07 and smaller than .2) and I'm dealing with a lot of them.
I've already tried the second approach but, as far as it seems, the function
doesn't satisfy my objective.
That's what I'm doing:
...
There are a couple of options.
First if you want the mean to equal 7, then that means the sum must
equal 21 and therefore you can let optim only play with 2 of the
variables, then set the 3rd to be 21-s1-s2.
If you want the mean to be greater than 7 then just put in a test, if
the mean is less th
Hi,
I'm dealing with an optimization problem. I'm using 'optim' to maximize the
output of a function, given some restrictions on the input. I would like to
know if there is a way to impose some restrictions on 'intermediate
variables' of the function. An example..
fx = function (x)
{
s <- 0
for (
_
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
Google for "R optimization&q
Diviya Smith wrote:
>
> Hi there,
>
> I have a complex math equation which does not have a closed form solution.
> It is -
>
> y <- (p*exp(-a*d)*(1-exp((d-p)*(a-x[1]/((p-d)*(1-exp(-p*(a-x[1]
>
> For this equation, I have all the values except for x[1]. So I need to
> solve
> this probl
Hi there,
I have a complex math equation which does not have a closed form solution.
It is -
y <- (p*exp(-a*d)*(1-exp((d-p)*(a-x[1]/((p-d)*(1-exp(-p*(a-x[1]
For this equation, I have all the values except for x[1]. So I need to solve
this problem numerically. Can anyone suggest an optimi
Kathie,
It is very difficult to help without adequate information. What does your
objective function look like? Are you maximizing (in which case you have to
make sure that the sign of the objective function is correct) or minimizing?
Can you try "optimx" with the control option all.methods=TR
00 (PDT)
> From: Kathie To: r-help@r-project.org Subject:
> [R]
> optimization problems Message-ID: <1313223129383-3741005.p...@n4.nabble.com>
> Content-Type:
> text/plain; charset=us-ascii Dear R users I am trying to use OPTIMX(OPTIM)
> for nonlinear
> optimization. Th
To be honest,
The first derivative of my objective function is very complicated so I
ignore this. Could it lead to this sort of problem?
Kathie
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Dear R users
I am trying to use OPTIMX(OPTIM) for nonlinear optimization.
There is no error in my code but the results are so weird (see below).
When I ran via OPTIM, the results are that
Initial values are that theta0 = 0.6 1.6 0.6 1.6 0.7. (In fact true vales
are 0.5,1.0,0.8,1.2, 0.6.)
--
Thankyou very much. I think "try" works for me.
I am learning it .
Sirius
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__
R-hel
On Jun 29, 2011, at 2:31 PM, siriustar wrote:
Hi, dear R help
I am trying to use optim inside a for loop:
##For example. a: intial guess. b: result. f: function to be
minimized
for (i in 1:10) {
b[i] <- optim(a[i], f)}
However, some intial values cause error in optim function (e.g. "
Hi, dear R help
I am trying to use optim inside a for loop:
##For example. a: intial guess. b: result. f: function to be minimized
for (i in 1:10) {
b[i] <- optim(a[i], f)}
However, some intial values cause error in optim function (e.g. " system is
computationally singular..."). Then the for
Dube, Jean-Pierre chicagobooth.edu> writes:
>
> To whom it may concern,
>
> I am trying to maximize a log-likelihood function using optim.
> This is a simple problem with only 18
> parameters. To conserve memory, I am using sparse matrices
> (SLAM) for some of the data matrices used in the
>
Dube, Jean-Pierre wrote:
>
> To whom it may concern,
>
> I am trying to maximize a log-likelihood function using optim. This is a
> simple problem with only 18 parameters. To conserve memory, I am using
> sparse matrices (SLAM) for some of the data matrices used in the
> computation of the lik
To whom it may concern,
I am trying to maximize a log-likelihood function using optim. This is a
simple problem with only 18 parameters. To conserve memory, I am using sparse
matrices (SLAM) for some of the data matrices used in the computation of the
likelihood. However, optim appears to co
Hello,
optim() works for more than one dimension. You might also find this
page helpful:
http://cran.r-project.org/web/views/Optimization.html
Cheers
Andrew
On Mon, May 02, 2011 at 12:41:19PM -0700, petrolmaniac wrote:
> Dear all,
>
> I am facing the following problem in optimization:
>
> w
Dear all,
I am facing the following problem in optimization:
w = (d, o1, ..., op, m1, ..., mq) is a 1 + p + q vector
I want to determine:
w = argmin (a - d(w))' A (a - d(w))
where a is a 1xK marix, A is the covariance matrix of vector a, d(w) is a
1xK vector which parameters are functions of
pkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Ben Bolker
Sent: Friday, March 25, 2011 3:23 PM
To: r-h...@stat.math.ethz.ch
Subject: Re: [R] a question on R optimiz
Paul Gilbert bank-banque-canada.ca> writes:
>
> It seems more likely that the return value from your function
> is NA or NaN or Inf. This might then result in an
> NA parameter value being calculated for the next step.
> This is possible, for example, because the line
> search extends outside
3:59 AM
> To: r-help@r-project.org
> Subject: [R] a question on R optimization functions
>
> Dear All,
>
> I use nlminb or optim for maximizing likelihood functions. Sometimes,
> the parameter values happen to be NA, then the program will hang there
> and iterate foreve
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