Thanks Ravi. Gildas
Ravi Varadhan a écrit : > I think the problem is because the the Hessian of the augmented Lagrangian > iis singular at c(0,0). > > Try this: > > require(alabama) > > heq <- function(x) { > x[1]^2+x[2]^2 - 1 > } > > >> constrOptim.nl(par=c(0,0), fn=f, heq=heq, control.outer=list(trace=FALSE)) >> > $par > [1] -0.7071067 -0.7071067 > > $value > [1] -1.414213 > > $iterations > [1] 10 > > $lambda > [1] -0.7068717 > > $penalty > [1] -6.496021e-08 > > $counts > function gradient > 100 30 > > > Ravi. > > ____________________________________________________________________ > > 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: Gildas Mazo <gildas.m...@curie.fr> > Date: Tuesday, August 10, 2010 10:11 am > Subject: [R] [Fwd: Re: optimization subject to constraints] > To: r-help@r-project.org > > > >> ----- Original Message ----- >> > > >> From Gildas Mazo <gildas.m...@curie.fr> >> > > >> Date Tue, 10 Aug 2010 15:49:19 +0200 >> > > >> To Matthias Gondan <matthias-gon...@gmx.de> >> > Subject Re: [R] optimization subject to constraints > >> Danke schön Matthias. >> >> I had naively started with x0 = c(0,0) and I got a "Redundant >> constraints were found" error. What's the problem with (0,0) ? >> >> >> >> >> >> >> >> Matthias Gondan a écrit : >> > try this (package Rsolnp) >> > >> > library(Rsolnp) >> > >> > g<- function(x) >> > { >> > return(x[1]^2+x[2]^2) >> > } # constraint >> > >> > f<- function(x) >> > { >> > return(x[1]+x[2]) >> > } # objective function >> > >> > x0 = c(1, 1) >> > >> > solnp(x0, fun=f, eqfun=g, eqB=c(1)) >> > >> > >> > >> > Am 10.08.2010 14:59, schrieb Gildas Mazo: >> >> Thanks, but I still cannot get to solve my problem: consider this >> simple >> >> example: >> >> >> >> ######## >> >> >> >> f<- function(x){ >> >> return(x[1]+x[2]) >> >> } # objective function >> >> >> >> g<- function(x){ >> >> return(x[1]^2+x[2]^2) >> >> } # constraint >> >> >> >> ######### >> >> >> >> I wanna Maximize f(x) subject to g(x) = 1. By hand the solution is >> >> (1/sqrt(2), 1/sqrt(2), sqrt(2)). This is to maximizing a linear function >> >> subject to a nonlinear equality constraint. I didn't find any suitable >> >> function in the packages I went through. >> >> >> >> Thanks in advance, >> >> >> >> Gildas >> >> >> >> >> >> >> >> >> >> >> >> Spencer Graves a écrit : >> >>> To find every help page containing the term "constrained >> >>> optimization", you can try the following: >> >>> >> >>> >> >>> library(sos) >> >>> co<- findFn('constrained optimization') >> >>> >> >>> >> >>> "Printing" this "co" object opens a table in a web browser >> with >> >>> all matches sorted first by the package with the most matches and >> with >> >>> hot links to the documentation page. >> >>> >> >>> >> >>> writeFindFn2xls(co) >> >>> >> >>> >> >>> This writes an excel file, with the browser table as the second >> >>> tab and the first being a summary of the packages. This summary >> table >> >>> can be made more complete and useful using the "installPackages" >> >>> function, as noted in the "sos" vignette. >> >>> >> >>> >> >>> A shameless plug from the lead author of the "sos" package. >> >>> Spencer Graves >> >>> >> >>> >> >>> On 8/9/2010 10:01 AM, Ravi Varadhan wrote: >> >>>> 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 >> >>>> [ 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 ? >> >>>> >> >>>> Dwayne >> >>>> >> >>>> 2010/8/9 Gildas Mazo<gildas.m...@curie.fr> >> >>>> >> >>>>> 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 >> >>>>> examples. >> >>>>> >> >>>>> Thanks for helping, >> >>>>> >> >>>>> Gildas >> >>>>> >> >>>>> ______________________________________________ >> >>>>> R-help@r-project.org mailing list >> >>>>> >> >>>>> PLEASE do read the posting guide >> >>>>> >> >>>> >> >>>> >> >>>> >> >>>> -guide.html> >> >>>>> and provide commented, minimal, self-contained, reproducible code. >> >>>>> >> >>>> [[alternative HTML version deleted]] >> >>>> >> >>>> ______________________________________________ >> >>>> R-help@r-project.org mailing list >> >>>> >> >>>> PLEASE do read the posting guide >> >>>> >> >>>> and provide commented, minimal, self-contained, reproducible code. >> >>>> >> >>>> ______________________________________________ >> >>>> R-help@r-project.org mailing list >> >>>> >> >>>> PLEASE do read the posting guide >> >>>> >> >>>> and provide commented, minimal, self-contained, reproducible code. >> >> >> > >> > ______________________________________________ >> > R-help@r-project.org mailing list >> > >> > PLEASE do read the posting guide >> > >> > and provide commented, minimal, self-contained, reproducible code. >> > >> > >> >> >> ______________________________________________ >> R-help@r-project.org mailing list >> >> PLEASE do read the posting guide >> and provide commented, minimal, self-contained, reproducible code. >> > > ______________________________________________ > 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. > > > ______________________________________________ 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.