The best solver for non-smooth functions?

There are obviously a large variety of non-smooth problems;
for CVAR problems, if by this you mean conditional value at
risk portfolio problems, you can use modern interior point 
linear programming methods.  Further details are here:

	http://www.econ.uiuc.edu/~roger/research/risk/risk.html

Roger Koenker
rkoenker@
# Hi, Roger.

# Unfortunately that "C" does not stand for
# "Conditional" but "Credit"... which means that
# risk measure is obtained via Monte Carlo
# simulated scenarios in order to quantify the
# credit loss according to empirical transition
# matrix. Then I am afraid of every solver finding
# local maxima (or minima) because of some
# "jump" in Credit VaR surface function of
# portfolio weights :(



On Jul 18, 2012, at 3:09 PM, Cren wrote:

# Whoops! I have just seen there's a little mistake
# in the 'sharpe' function, because I had to use
# 'w' array instead of 'ead' in the cm.CVaR function!
# This does not change the main features of my,
# but you should be aware of it

---

# The function to be minimized

sharpe <- function(w) {
 - (t(w) %*% y) / cm.CVaR(M, lgd, ead, N, n, r, rho, alpha, rating)
} 

# This becomes...

sharpe <- function(w) {
 - (t(w) %*% y) / cm.CVaR(M, lgd, w, N, n, r, rho, alpha, rating)
} 

# ...substituting 'ead' with 'w'.

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There are obviously a large variety of non-smooth problems;
for CVAR problems, if by this you mean conditional value at
risk portfolio problems, you can use modern interior point 
linear programming methods.  Further details are here:

	http://www.econ.uiuc.edu/~roger/research/risk/risk.html

Roger Koenker
rkoenker@