Optimization of constrained linear least-squares problem

Dear R-ians,

I want to perform an linear unmixing of image pixels in fractions of 
pure endmembers. Therefore I need to perform a constrained linear 
least-squares problem that looks like :

min || Cx - d || ? where sum(x) = 1.

I have a 3x3 matrix C, containing the values for endmembers and I 
have a 3x1 column vector d (for every pixel in the image). In theory 
my x values should all be in the (0,1) interval but I don't want to 
force them so I can check the validity of my solution. I just want 
to calculate the x values. Can anyone help me with this problem? 
I've been checking the optim, optimize, constrOptim and nlm help 
files, burt  I don't understand it very well. Wich function should I 
use for my problem? I did a first test using optim:

# Make my C matrix
EM<- 
c(4.5000,6.0000,10.5000,5.0000,27.0000,20.7500,16.7500,23.6666,38.7500)
C <- array(EM, c(3,3))

# Take an arbitrary d
d<-c(10, 20, 20)

# Define the function
fr <- function(x) {
C[1,]*x=d
C[2,]*x=d
C[3,]*x=d
sum(x)=1}

# Perform the optimization
optim(c(0.25,0.25,0.25),fr)

But it did not work. I got the eror couldn't find function. Can 
anyone tell me what functyion I should use for my problem and how 
should I program it?

Thanx in advance,
Stef