Hi,
I'm trying to do some constrained non-linear optimisation, but my
function does not have second order derivatives everywhere.
To be a little more specific (the actual function is huge and
horrible, so it would probably be better to just describe it) my model
has four variables and I'm using op
This looks much like the kinds of problems that function 'solve.QP' in
package 'quadprog' can handle. But if you want people to help you,
follow the posting guide and 'provide commented, minimal,
self-contained, reproducible code'. You need to show people what you
tried and how it failed (so
try the lsei function from the limSolve package.
On Mon, Dec 19, 2011 at 2:32 PM, Russell2 wrote:
> Dear All
>
> I have a constrained optimisation problem, I want to maximise the following
> function
>
> t(weights) %*% CovarianceMatrix %*% weights
>
> for the weights,
>
> subject to constraints
Dear All
I have a constrained optimisation problem, I want to maximise the following
function
t(weights) %*% CovarianceMatrix %*% weights
for the weights,
subject to constraints on each element within the weights & the weights
vector summing to 1.
i.e.
weights = (x1, x2, x3), where x1 is w
02, 2009 3:32 PM
To: 'Iason Christodoulou'
Cc: r-help@r-project.org
Subject: Re: [R] constrained optimisation in R.
Hi,
I know nothing about neither your model nor the Skellam distribution. I
will assume that it is a sensible model and that the parameters are
identifiable from the data.
h/People/Faculty_personal_pages/Varadhan.h
tml
_
From: Iason Christodoulou [mailto:c_iaso...@hotmail.com]
Sent: Thursday, July 02, 2009 2:58 PM
To: rvarad...@jhmi.edu
Subject: RE: [R] constrained optimisation in R.
$B&3(Bhe actual problem is:
Log ($B&K(
ursday, July 02, 2009 11:56 AM
To: r-help@r-project.org
Subject: [R] constrained optimisation in R.
i want to estimate parameters with maximum likelihood method with contraints
(contant numbers).
for example
sum(Ai)=0 and sum(Bi)=0
i have done it without the constraints but i realised that i have
i want to estimate parameters with maximum likelihood method with contraints
(contant numbers).
for example
sum(Ai)=0 and sum(Bi)=0
i have done it without the constraints but i realised that i have to use the
contraints.
Without constraints(just a part-not complete):
skellamreg_LL=function(pa
On Wed, Mar 12, 2008 at 11:29 AM, giovanna menardi
<[EMAIL PROTECTED]> wrote:
> i have to optimise a function f(a,b), with a, b vectors in R^d such that a
> and b are orthogonal, that is a'b=0. Anybody has a suggestion?
And your function f(a,b) is defined by?
Paul
_
Hi,
i have to optimise a function f(a,b), with a, b vectors in R^d such that a and
b are orthogonal, that is a'b=0. Anybody has a suggestion?
Thanks, in advance, for your help,
Giovanna
_
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