similar to: quadratic programming-maximization instead of minization

Dear R Help, I am trying to fit a nonlinear model for a mean function $\mu(Data_i, \beta)$ for a fixed covariance matrix where $\beta$ and $\mu$ are low- dimensional. More specifically, for fixed variance-covariance matrices $\Sigma_{z=0}$ and $\Sigma_{z=1}$ (according to a binary covariate $Z $), I am trying to minimize: $\sum_{i=1^n} (Y_i-\mu_(Data_i,\beta))' \Sigma_{z=z_i}^{-1} (Y_i-

Hello, # Full disclosure. I am not sure if my problem is a bug(s) in the code, or a fundamental misunderstanding on my part about what I am trying to do with these statistics. I am not familiar with maximum likelihood tests. # I currently have two vectors Aequipecten<-c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,

Dear R experts, Attached you can find the expression of a loglikelihood function which I would like to maximize in R. So far, I have done maximization with the combined use of the mathematical programming language AMPL (www.ampl.com) and the solver SNOPT (http://www.sbsi-sol-optimize.com/manuals/SNOPT%20Manual.pdf). With these tools, maximization is carried out in a few seconds. I wonder if that

Dear community, I am currently trying to fit an ordinal logistic regression model with the polr function. I often get the same error message : "attempt to find suitable starting values failed", for example with : require(MASS) data(iris) polr(Species~Sepal.Length+Sepal.Width+Petal.Length+Petal.Width,iris) (I know the response variable Species should be nominal but I do as levels

Dear R Users I am maximizing a user defined log likelihood function. It includes variance parameter (sigma). I used R function optim with BFGS maximization method. However, it stops before the solution saying ?sqrt(sigma): NaNs produced? Could anybody know a proper transformation for sigma which can be passed in the function? For the correlation parameter I used Fishers? transformation so it

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

Here a patch to enable Vertical/Horizontal maximization in gtk-window-decorator. Cedric -------------- next part -------------- A non-text attachment was scrubbed... Name: gtk-window-decorator_HorzVertMaximize.patch Type: text/x-diff Size: 1418 bytes Desc: not available Url : http://lists.freedesktop.org/archives/compiz/attachments/20070325/22d15360/gtk-window-decorator_HorzVertMaximize.bin

Hello: I have been trying to program the following maximization problem and would definitely welcome some help. the target function: sum_{i} f(alpha, beta'X_{i}), where alpha and beta are unknown d-dim parameter, f is a known function an X_{i} are i.i.d. r.v. I need to maximize the above sum, under the constaint that:

Hello, here is an example from the manual. How to turn this minimization problem into maximization problem, i.e. -(0 5 0) %*% b - 1/2 b^T b? # Assume we want to minimize: -(0 5 0) %*% b + 1/2 b^T b # under the constraints: A^T b >= b0 # with b0 = (-8,2,0)^T # and (-4 2 0) # A = (-3 1 -2) # ( 0 0 1) # we can use solve.QP.compact as follows: # library(quadprog) Dmat <- matrix(0,3,3)

Hello, I would like to know if there's a package in R to solve the following problem: Let's consider a cloud of points in a n-dimensional space. Each point is associated to a specific value Vi (a real that can be positive or negative). I would like to find the n-dimensional hypercube that maximizes the sum of Vi corresponding to the points inside of the hypercube. How would you solve

hi people, I'm trying to maximize this function: fn= function (x) {x[1]^2+5*x[2]^2} with this restriction fn1 = function (x) {x[1]+x[2] <=5} Can someone help me how to procedure this? I tried in the alabama and genoud package but i have problems with the setting of constrains. Regards, Eliano -- View this message in context:

Dear all, I would like to solve the following problem, which can be done with optimal control theory or dynamic programming: max(x,y) a*u1+b*u2+c*f1(u2) s.t. 0<u1<x, 0<u2<f2(x,u2), x'=f3(u1,u2,x) which can be rewritten if optimal control theory should be applied as H=a*u1+b*u2+c*f1(u2)+lambda*(x') s.t. 0<u1<x, 0<u2<f2(x,u2) The maximum principle

Given a vector : pvec=(p1,p2,.... p J) with sum(pvec)=1, all the elements are non-negative, that is, they are probabilities a matrix A ( N* J ), with the elements alpha(ij) are 0 or 1 I want to MAXIMIZE THE RESULT RESULT= product( i=1, to N [ sum ( alpha(ij)* pj , j =1,to J ) ] ) thus, I need to get pvec. how should I do ? for example

Job #1 for me with CentOS 7 is to disable the automatic window maximization. Some googling found this command: $ gsettings set org.gnome.mutter auto-maximize false No such schema 'org.gnome.mutter' and this: $ gsettings set orh.gnome.shell.overrides edge-tiling false but that had no visible effect. I couldn't find anything under Applications->documentation and I didn't see

Hi, R Users; I am trying to maximize a likelihood function which contains an integral. The integral contains the unknown parameter as well. I am trying to use the following code to do the maximization: ll<-function(b.vec){ b0<-b.vec[1] b1<-b.vec[2] b2<-b.vec[3] p<-1/(1+exp(-b0-b1*z1-b2*x2))

I''m looking for a R function which can maximise this logliklihood function, under the constraits a>0 e b>0 f<-function(param){ a<-param[1] b <-param[2] log(prod)-(a*s2)-(b*s)-n*log(1-((0.5*b/sqrt(a))*(exp((b^2)/(4*a)))*((sqrt(pi ))*(1-pnorm(-b/(2*sqrt(a)), mean=0, sd=1)))))} I''ve tried maxlik constrOptim e donlp2 but without success. Thanks so

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,

Dear All, First of all, this is the first time for me to use R for optimization, I tried to search r-help postings & googled on weighted sum optimization, I could not find anything applicable. I would need to optimize following function in R; MAXIMIZE function = w1*R1 + w2*R2 + w3*R3 + w4*R4 Where constraints are, w1 + w2 + w3 + w4 = 1 and 0 <= w1, w2, w3, w4 <= 1 Does optim

Hello, all, I had problems with an extension to a classic optimization problem. The target is to minimize a quadratic form a'Ma with respect to vector b, where vector a=(b',-1)', i.e., a is the expand of b, and M is a symmetric matrix (positive definite if needed). One more constrain on b is b'b=1. I want to solve b given M. I tried but it seems impossible to find an analytic

A bioligist colleague sent me the following data. x Y 3 1 7 5 14 8 24 0 (Yes, only four data points.) I don't know much about the application, but apparently there are good empirical reasons to use a quadratic model. The goal is to find the X value which maximizes the response Y, and to find a confidence interval for this X value. Finding the maximizing X value is pretty