quadprog

I was wondering what's the proper way to implement Quadprog to solve quadratic programming. I have the following question(ripped from the internet)and also was looking at the following http://cbio.ensmp.fr/~thocking/mines-course/2011-04-01-svm/svm-qp.pdf What would be the proper way to solve this issue? Would this tutorial be useful to solve if i was given a question like above? http://www.r-bloggers.com/solving-quadratic-progams-with-rs-quadprog-package/ Here is an implementation, for linear C-SVM, which is based on the primal optimization problem: min_{beta_0, beta, zeta} 1/2 w^T w + C sum_

The documentation for portfolio.optim {tseries} says that solve.QP {quadprog} is used to generate the solution for finding the tangency portfolio that maximizes the Sharpe ratio. That implies that results should be identical with either function. I'm probably overlooking something, but in this simple example I get similar but not identical solutions for estimating optimal portfolio weights with portfolio.optim and solve.QP. Shouldn't the results be identical? If so, where am I going wrong? Here's the code: library(tseries) library(quadprog) # 1. Generate solution with solve.QP via: comisef

With a lot of help from contributors to StackOverflow I have managed to put together a function to derive the weights of a 2-asset portfolio which maximises the Sharpe ratio. No short sales are allowed and the sum of weights add to 1. What I would like to do now is to constrain asset A to not being more or less than 10% from a user defined weight. As an example I would like to constrain the weight of asset A to be no less than 54% or more than 66% (i.e 60% +/- 10%). So on the below example I would end up with weights of (0.54,0.66) instead of the unsconstrained (0.243,0.7570) .I assume this