# [R] The best solver for non-smooth functions?

Cren oscar.soppelsa at bancaakros.it
Thu Jul 19 10:23:48 CEST 2012

```Roger Koenker-3 wrote
>
> 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
>> # 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'.
>>
>> --
>> View this message in context:
>> http://r.789695.n4.nabble.com/The-best-solver-for-non-smooth-functions-tp4636934p4636936.html
>> Sent from the R help mailing list archive at Nabble.com.
>>
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>> and provide commented, minimal, self-contained, reproducible code.
>
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