search for: controptim

Hello. I had previously posted a question concerning the optimization of a nonlinear function conditional on equality constraints. I was pointed towards the contrOptim function. However, I do not understand the syntax of this function with respect to specifying the constraints and so I don’t know if it is what I need. The command is: constrOptim(theta, f, grad,ui,ci,…). “theta” is the initial value of the variables in the function, “f” and “grad” are the func...

...gain but the same warning message pops up. Could you please point me a way out of this? I am actually trying to translate a fortran code to R and the function I want to replace is DBCPOL, which used 'the complex method" described in Nelder and Mead (1965) and Gill et al. (1981). I believe contrOptim is better than it because it is newer, is it? Best Wishes Yuchen Luo [[alternative HTML version deleted]]

Hi R-list, I am new to optimization in R and would appreciate help on the following question. I would like to minimize the following function using two constraints: ###### fn <- function(par,H,F){ fval <- 0.5 * t(par) %*% H %*% par + F%*% par fval } # matrix H is (n by k) # matrix F is (n by 1) # par is a (n by 1) set of weights # I need two constraints: # 1.

...... 0 . . . ... 0 0 0 0 ... 1 and c_i as (-1,0,...,0). Then whatever value \beta_0 takes, the first value of u_i%*%theta_i is always zero, which automatically is larger than -1. That is how I construct u_i and c_i. contrOptim returns the barrier.value. I am wondering whether the value corresponds to -1*\mu*(\sum_{i=1}^p log(\hat{\beta_i}-0)+\log(1)). I need to get the information matrix which also takes into account the additional barrier term so I need to figure out what the value of \mu is. Assuming what I wrote down...

Hi, I apologize for the long message but the problem I encountered can't be stated in a few lines. I am having some problems with the function constrOptim. My goal is to maximize the likelihood of product of K multinomials, each with four catagories under linear constraints on the parameter values. I have found that the function does not work for many data configurations. #The likelihood