[R] optimize linear function

[Michaell Taylor]

I am attempting to optimize a regression model's parameters to meet a specific 
target for the sum of positive errors over sum of the dependent variable 
(minErr below).  

I see two courses of action , 1) estimate a linear model then iteratively 
reduce the regressors to achieve the desired positive error threshold 
(naturally the regressors and predicted values are biased - but this is 
acceptable).  Were the problem only a single independent variable problem, 
this approach would be fairly trivial.  But the two variable model proves 
more troublesome from an efficiency point of view (efficiency being the mean 
negative error for a given minErr).

The second method, which seems more efficient, is to optimize the linear 
regressors until the criteria is met.  There are several optimizer packages 
out there, but none seem particularly well suited to this sort of task.  

Any suggestions out there?

===========  Sample Problem = first approach  ================

# if we say... 
B1 <- .8
B2 <- .2

# then construct some data..
x1 <- rnorm(100,mean=100,sd=20)
x2 <- rnorm(100,mean=10,sd=2)
y <- B1*x1+B2*x2+rnorm(100,mean=5,sd=10)
# normally of course B1 and B2 are unknown to begin with

# linear model  (presumably yielding B1=.8 and B2=.2)
m<-lm(y ~ x1+ x2)
# test on summation of positive errors.
e <- resid(m)
minErr <- (sum(ifelse(e<0,0,e))/sum(y))-.03
while (minErr>.03){
	Make some adjustment to B1 and B2
	minErr <- (sum(ifelse(e<0,0,e))/sum(y))-.03
	}

======== Second Approach ===========

construct a function to minimize minErr with beginning values from 'm'.


Any suggestions for approaches greatly appreciated.