[R] Optimization in R similar to MS Excel Solver

Richard James Richard.J.Cooper at uea.ac.uk
Fri Oct 26 12:50:59 CEST 2012

Dear Berend and Thomas,

thank you for suggesting the lsei function. I found that the tlsce {BCE}
function also works very well:


The limSolve package has an 'xsample' function for generating uncertainty
values via Monte-Carlo simulation, however it only works when specifying the
standard deviation on the target data (B). In my situation I have standard
deviations for the source areas (A) only. Therefore I need to generate the
uncertainty values manually.

I've created a matrix of 1000 randonly distributed numbers for each of the
source area (A) properties:
TSCa<-matrix(rnorm(1000, mean=0.03, sd=0.005),ncol=1)
TSMg<-matrix(rnorm(1000, mean=0.0073, sd=0.002),ncol=1)
CBCa<-matrix(rnorm(1000, mean=0.6, sd=0.1),ncol=1)
CBMg<-matrix(rnorm(1000, mean=0.0102, sd=0.005),ncol=1)
RCa<-matrix(rnorm(1000, mean=0.2, sd=0.05),ncol=1)
RMg<-matrix(rnorm(1000, mean=0.0141, sd=0.005),ncol=1)
DCa<-matrix(rnorm(1000, mean=0.35, sd=0.1),ncol=1)
DMg<-matrix(rnorm(1000, mean=0.012, sd=0.004),ncol=1)


I now want to run the lsei model again:


but with A=bmat replaced by the appropriate random values in DistAll.  I
assume I could then use the function "replicate" to then run the model 1000
times to generate uncertainty values, e.g.


Would anyone be able to help me write a function to replace bmat with new
values from DistAll each time the lsei model is run?

Many thanks in advance


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