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
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
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
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
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
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
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
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
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!
> 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?
Dennis
On Sat, Nov 16, 2013 at 1:26 PM, Prof J C Nash (U30A)
There are lots of errors in your code. In particular, the optimization
routines do not like functions that ignore the parameters.
And you have not provided out or out1 to the optimizer -- they are
returned as elements of func(), but not correctly.
Please try some of the examples for optim or opti
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
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
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: ##.#. ##.#.
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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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]
On 12-03-2013, at 08:45, Pavel_K wrote:
> Dear Mr Hasselman,
> for a better understanding I have attached an example solved in excel by
> using the tool Solver.
>
> I want to assign for each municipality one of the centres and apply it for
> calculating the minimum cost as you can see in an exa
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
Dear Mr Hasselman,
for a better understanding I have attached an example solved in excel by
using the tool Solver.
I want to assign for each municipality one of the centres and apply it for
calculating the minimum cost as you can see in an example.
I used package lpsolve, but it does not work. I a
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
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
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
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
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
On 09/14/2011 10:37 PM, 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 so
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
optimx with BFGS uses optim, so you actually incur some overhead unnecessarily.
And BFGS
really needs good gradients (as does Rvmmin and Rcgmin which are updated BFGS
and CG, but
all in R and with bounds or box constraints).
>From the Hessian, your function is (one of the many!) that have pretty
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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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. "
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
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
du
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Ravi Varadhan
Sent: Monday, February 14, 2011 10:20 AM
To: 'Gabrielsen, Alexandros'; r-help@r-project.org
Subject: Re: [R] Optimization Question
Your function is non-smooth
Your function is non-smooth and nasty looking. You might want to set the
function value to a large positive number if an illegal arithmetic operation
is performed and `NaN' is returned.
fn <- function(p) {
ftemp <- 263*log(sqrt(2*pi)*sd(test$A))+ sum(log(abs(c(test$A[-1],
1))^p[3])) + (sum(((te
On Oct 5, 2010, at 2:46 PM, QiJun Fung wrote:
Does any one know how to optimize the following function w.r.t to
beta? The difficulty here is the beta is a matrix not a vector. and f
is also a function of beta and an element of the objective function. I
just want to know for this complicated sit
Ravi.
-Original Message-
From: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] On
Behalf Of Dwayne Blind
Sent: Monday, August 09, 2010 12:56 PM
To: Gildas Mazo
Cc: r-help@r-project.org
Subject: Re: [R] optimization subject to constraints
Hi !
Why not constrOptim ?
D
:
>> constrOptim can only handle linear inequality constraints. It cannot
>> handle
>> equality (linear or nonlinear) as well as nonlinear inequality
>> constraints.
>>
>> Ravi.
>>
>> -Original Message-----
>> From: r-help-boun...@r-project.org
>
not handle
equality (linear or nonlinear) as well as nonlinear inequality constraints.
Ravi.
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Dwayne Blind
Sent: Monday, August 09, 2010 12:56 PM
To: Gildas Mazo
Cc: r-help@r-projec
: Monday, August 09, 2010 12:56 PM
To: Gildas Mazo
Cc: r-help@r-project.org
Subject: Re: [R] optimization subject to constraints
Hi !
Why not constrOptim ?
Dwayne
2010/8/9 Gildas Mazo
> Dear R users,
>
> I'm looking for tools to perform optimization subject to constraints,
> bo
try command solnp in package Rsolnp
Am 09.08.2010 18:56, schrieb Dwayne Blind:
Hi !
Why not constrOptim ?
Dwayne
2010/8/9 Gildas Mazo
Dear R users,
I'm looking for tools to perform optimization subject to constraints,
both linear and non-linear. I don't mind which algorithm may be used, m
You may want to look at:
http://cran.r-project.org/web/packages/alabama/index.html
Ravi.
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Gildas Mazo
Sent: Monday, August 09, 2010 12:49 PM
To: r-help@r-project.org
Subject: [R] optim
Hi !
Why not constrOptim ?
Dwayne
2010/8/9 Gildas Mazo
> Dear R users,
>
> I'm looking for tools to perform optimization subject to constraints,
> both linear and non-linear. I don't mind which algorithm may be used, my
> primary aim is to get something general and easy-to-use to study simples
Sent: Wednesday, July 28, 2010 11:11 AM
To: r-h...@stat.math.ethz.ch
Subject: Re: [R] Optimization problem with nonlinear constraint
Uli Kleinwechter uni-hohenheim.de> writes:
>
> Dear Ravi,
>
> As I've already written to you, the problem indeed is to find a solution
>
--
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Hans W Borchers
Sent: Wednesday, July 28, 2010 11:11 AM
To: r-h...@stat.math.ethz.ch
Subject: Re: [R] Optimization problem with nonlinear constraint
Uli Kleinwechter uni-hohenheim.de> writes:
>
> Dear
Uli Kleinwechter uni-hohenheim.de> writes:
>
> Dear Ravi,
>
> As I've already written to you, the problem indeed is to find a solution
> to the transcendental equation y = x * T^(x-1), given y and T and the
> optimization problem below only a workaround.
I don't think optimization is the ri
Dear Ravi,
As I've already written to you, the problem indeed is to find a solution
to the transcendental equation y = x * T^(x-1), given y and T and the
optimization problem below only a workaround.
John C. Nash has been so kind to help me on here. In case anyone faces a
similar problem in
Hi Uli,
I am not sure if this is the problem that you really want to solve. The
answer is the solution to the equation y = x * T^(x-1), provided a solution
exists. There is no optimization involved here. What is the real problem
that you are trying to solve?
If you want to solve a more meaning
> I don't see why one would want to pretend that the function is continuous.
It isn't.
> The x variable devices is discrete.
> Moreover, the whole solution space is small: the possible solutions are
integers in the range of maybe 20-30.
Yes, you are right, what I'd like to think is that the outco
I don't see why one would want to pretend that the function is
continuous. It isn't.
The x variable devices is discrete.
Moreover, the whole solution space is small: the possible solutions
are integers in the range of maybe 20-30.
Bill
On Fri, Jun 18, 2010 at 9:00 AM, José E. Lozano wrote:
>
>>>
>> How about smoothing the percentages, and then take the second
>> derrivative to find the inflection point?
>>
>> which.max(diff(diff((lowess(percentages)$y
>
> This solution is what I've been using so far. The only difference is that
I am smoothing the 1st derivative, since its
> the one
Hello:
> Here is a general approach using smoothing using the Gasser-Mueller
kernel,
> which is implemented in the "lokern" package. The optimal bandwidth for
> derivative estimation is automatically chosen using a plug-in
approximation.
> The code and the results are attached here.
Maybe am I
> How about smoothing the percentages, and then take the second derrivative
to find the inflection point?
>
> which.max(diff(diff((lowess(percentages)$y
This solution is what I've been using so far. The only difference is that I
am smoothing the 1st derivative, since its the one I want to be s
Here is a general approach using smoothing using the Gasser-Mueller kernel,
which is implemented in the "lokern" package. The optimal bandwidth for
derivative estimation is automatically chosen using a plug-in approximation.
The code and the results are attached here.
Let me know if you have any
min(devices[percentages==max(percentages)])
Bill
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How about smoothing the percentages, and then take the second derrivative to
find the inflection point?
which.max(diff(diff((lowess(percentages)$y
Bart
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Berend Hasselman wrote:
>
>
> Bogaso wrote:
>>
>> Hi all, I need to minimize following function :
>>
>> dat <- matrix(rnorm(2), ncol=2)
>> targetFn <- function(x) {
>> dat <- as.matrix(dat)
>> dat1 <- 1*dat[,1] - (x^2)*dat[,2]
>> return(sd(dat1))
Bogaso wrote:
>
> Hi all, I need to minimize following function :
>
> dat <- matrix(rnorm(2), ncol=2)
> targetFn <- function(x) {
> dat <- as.matrix(dat)
> dat1 <- 1*dat[,1] - (x^2)*dat[,2]
> return(sd(dat1)) }
>
> i.e. I want ro find for which "x"
Well, Albyn Jones gave a great solution to my challenge that found the best
reading schedule.
My original thought was that doing an exhaustive search would take too much
time, but Albyn showed that there are ways to do it efficiently.
My approach (as mentioned before) was to use optim with meth
Ravi Varadhan jhmi.edu> writes:
>
> Dear Hans,
>
> I agree with your comments. My intuition was that the quadratic
> form would be better behaved than the radical form (less
> nonlinear!?). So, I was "hoping" to see a change in behavior when
> the cost function was altered from a radical (i.
gt; Ravi Varadhan, Ph.D.
> Assistant Professor,
> Division of Geriatric Medicine and Gerontology
> School of Medicine
> Johns Hopkins University
>
> Ph. (410) 502-2619
> email: rvarad...@jhmi.edu
>
>
> - Original Message -
> From: Erwin Kalvelagen
&
klau...@gmx.de
Date: Sunday, January 17, 2010 8:06 am
Subject: Re: [R] optimization problem
To: Ravi Varadhan , erwin.kalvela...@gmail.com,
hwborch...@googlemail.com
Cc: r-h...@stat.math.ethz.ch
> Dear Erwin, Ravi and Hans Werner,
>
> thanks a lot for your replies. I don't think
atric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
- Original Message -
From: "Hans W. Borchers"
Date: Sunday, January 17, 2010 3:54 am
Subject: Re: [R] optimization problem
To: r-h...@stat.math.eth
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