similar to: minimization a quadratic form with some coef fixed and some constrained

Hello, all, It is a general question, but I couldn't find the answer elsewhere. I am using an R function using .C but don't understand one of its behaviors without the C code. I am wondering the so-called 'open source'. It doesn't include the C code together with the R function, does it? So what I want to ask is whether it is justified, possible or polite to ask for the C

Hello all, I have a data frame in R, imported from an excel file in Swedish. The original file contains several columns that have special characters, such as \¨{a}, \¨{o}, and so on. After import such special characters are represented in the data frame by "\\345", "\\366" etc (don't ask me why). For example, a word "Hårkan" becomes ''H\\345rkan".

Dear R developers, The trace of the hat matrix H~(n,n) is computed as follows: tr(H) = tr(BS^-1B') = tr(S^-1B'B) := tr(X) = sum(diag(X)) with B~(n,p), S~(p,p). Since p is of the order 10^3 but S is sparse I would like to employ Taucs linear solver ( http://www.tau.ac.il/~stoledo/taucs/ ) on SX = B'B. (Further improvement by implying a looping over i=1,...,p, calling

Hello, there!   Basically my problem is very clear. I would like to take a (numerical) integration of a function f(x,y) which can be quite complex of x and y, over a disk (x-a)^2+(y-b)^2<= r^2 (with r constant). However, after some search in R, I just cannot find a function in R that suits my purpose. Function Integrate applies to one dimensional, and adaptIntegrate to rectangle. In my case, it

Dear R-users, I would like to know if there is any way to minimize || y - Xb||^2 under the constraint b'b=1. Thank you Giancarlo

(renaming subject as I am partially getting off-topic) On Sunday 10 August 2014 16:26:07 Richard W.M. Jones wrote: > > The next issue I see now is about the value_value function. This is > > briefly documented as: "return data length, data type and data of a > > value". > > > > For Perl, Python and OCaml, this is not true. A tuple is returned > >

Dear Peter, thank you very much for your answer. My problem is that I need to calculate the following quantity: solve(chol(A)%*%Y) Y is a 3*3 diagonal matrix and A is a 3*3 matrix. Unfortunately one eigenvalue of A is negative. I can anyway take the square root of A but when I multiply it by Y, the imaginary part of the square root of A is dropped, and I do not get the right answer. I tried

Hi, Does anybody have experience to solve an quadratic programming problem with quadratic constraints in R? It seems that the package "quadprog" only handles the quadratic programming with linear constraint. My probelm is to maximze x^T\Sigma_{xy} y, subject to x^Tx=1, y^T\Sigma_{yy} y=1, and sum(y)<t, or sum(y)=t, where x and y are the variable, and the Sigma's and t are

Hi all, Could anybody please point me to the solver function in R on QCQP? The quadprog package seems to be only able to handle the linear constraints... Thank you! [[alternative HTML version deleted]]

Hello, S-plus and R helpers,(sorry for cross-post) I wrote some simple C code for one likelihood to be optimized (using optim(MASS)). I use same function, same data, same starting points and same DLL in R and S-plus for comparison. (I compiled it with 'Rcmd SHLIB likelihood.c' and the header files of it include only R.h and math.h). While it works quite fine in S-plus, it forever returns

I want to compute B=A^{1/2} such that B*B=A. For example a=matrix(c(1,.2,.2,.2,1,.2,.2,.2,1),ncol=3) so > a [,1] [,2] [,3] [1,] 1.0 0.2 0.2 [2,] 0.2 1.0 0.2 [3,] 0.2 0.2 1.0 > a%*%a [,1] [,2] [,3] [1,] 1.08 0.44 0.44 [2,] 0.44 1.08 0.44 [3,] 0.44 0.44 1.08 > b=a%*%a i have tried to use singular value decomposion > c=svd(b) > c$u%*%diag(sqrt(c$d))

Dear All, My question is simple but I need someone to help me out. Suppose I have a positive definite matrix A. The funtion chol() gives matrix L, such that A = L'L. The inverse of A, say A.inv, is also positive definite and can be factorized as A.inv = M'M. Then A = inverse of (A.inv) = inverse of (M'M) = (inverse of M) %*% (inverse of M)' = ((inverse of

Dear experts! I have a minimization problem with non-linear constraint and Objective function(theta)=lambda*(Constr)^2-f(x,theta). Theta is a vector of parameters. I'd like to stop the optimization after the value of the constraint is less or equal some constant value, say d, and save the last computed value of the function. For this purpose, I thought to define the Objective function like

Hi all, I want to use tabu search to solve my minimization problem. but tabu search in R is for maximization, so I turn my function from f to -f? but the eUtilityKeep always be 0 from the second position. I have go through a part of source code found that it always give the default value to compare, move <- ifelse(maxTaboo > maxNontaboo & maxTaboo > aspiration,

Hi, I hope someone can help me with the following issue. I need find the minimum beta that satisfies the following: inf{beta>0 | f(x+beta*f(x))*f(x)<=0} where f() is a function and x is a sample statistic. Functions such as "nlminb" and "constrOptim" minimize a function and output the parameter (under parameter constraints). I need to minimize the parameter (also

Good evening! I have a multi-dimensional minimization problem whose gradient is pretty hard to code. I tried Nelder and Mead method implemented in function optim and it does not work well. I also tried the quasi Newton method in optim using difference as approximate derivative. It does not work well either. I just went through Numerical Recipes book. The book discusses another method without

Hello all, I am trying to minimize a function which contains a double integral, using "nlminb" for the minimization and "adapt" for the integral. The integral is over two variables (thita and radiusb) and the 3 free parameters I want to derive from the minimization are counts0, index and radius_eff. I have used both tasks in the past successfully but this is the first time

Hi everyone, i?m a new user of R and i?m trying to translate an linear optimization problem from Matlab into r. The matlab code is as follow: options = optimset('Diagnostics','on'); [x fval exitflag] = linprog(f,A,b,Aeq,beq,lb,ub,[],options); exitflag fval x=round(x); Where: f = Linear objective function vector (vector of 45,rows) A = Matrix for linear inequality

Dear all, I'm working on the following problem: Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset X contains values of Q after an event A while dataset Y contains values of Q after an event B. In R X, Y are vectors of the same length, containing effectivelly a number of observations of Q in each state. Q is a continous variable. Now, the two datasets should

Hi, I got a code from S that uses 'nlminb' to minimize a function with constraints. Is there a similar function in R? Thanks. R. Heberto Ghezzo Ph.D. Meakins-Christie Labs McGill University Montreal - Canada heberto at meakins.lan.mcgill.ca -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read