<div dir="ltr">It will be nice if there is a simple way to formulate this as an LP problem to maximize mean/ETL. If there is not a simple formulation, one way to approach this would be similar to finding the tangency portfolio on the efficient frontier. Generating a finite number of portfolios along the frontier and finding the portfolio with the highest mean/ETL will find the approximate tangency portfolio and is what I do for the efficient frontier code.<div>
<br></div><div>Step 1: Calculate the minimum ETL portfolio given the constraints. This is the minimum possible mean return.</div><div><br></div><div>Step 2: Calculate the maximum return portfolio given the constraints. This is the maximum possible mean return.</div>
<div><br></div><div style>Step 3: Increase or decrease the target return constraint and run the optimization.</div><div><div><br></div><div>Repeat step 3 until we get convergence within a specified tolerance or reach the maximum number of iterations.</div>
</div><div><br></div><div style>I'm not sure what the right approach or method would be for step 3. Maybe split the frontier in two equal spaces and iteratively shrink the search space until we find a solution. </div>
<div style><br></div><div style>Am I on the right track here? Any thoughts on how to do step 3?</div><div style><br></div><div style>Thanks,</div><div style>Ross</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">
On Sun, Oct 6, 2013 at 8:44 AM, Doug Martin <span dir="ltr"><<a href="mailto:martinrd@comcast.net" target="_blank">martinrd@comcast.net</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Will need to do an in-depth comparison of Rglpk versus Symphony LP (withMIP)<br>
solvers.<br>
<br>
I think you mentioned a project for evaluating the various solvers against<br>
commonly used benchmark problems? What is the status and timing of that?<br>
<div class="im HOEnZb"><br>
Doug<br>
<br>
<br>
<br>
-----Original Message-----<br>
From: <a href="mailto:gsoc-porta-bounces@lists.r-forge.r-project.org">gsoc-porta-bounces@lists.r-forge.r-project.org</a><br>
[mailto:<a href="mailto:gsoc-porta-bounces@lists.r-forge.r-project.org">gsoc-porta-bounces@lists.r-forge.r-project.org</a>] On Behalf Of Brian<br>
G. Peterson<br>
</div><div class="im HOEnZb">Sent: Sunday, October 06, 2013 8:19 AM<br>
To: <a href="mailto:gsoc-porta@r-forge.wu-wien.ac.at">gsoc-porta@r-forge.wu-wien.ac.at</a><br>
Subject: Re: [GSoC-PortA] Mean-mETL objective?<br>
<br>
</div><div class="HOEnZb"><div class="h5">On 10/06/2013 10:00 AM, Doug Martin wrote:<br>
> P.S. Chapter 4 in the 2nd edition on mean-ETL optimization via LP with<br>
> Rglpk, with some nice examples (will send when available). I will also<br>
use<br>
> this for the MIP examples in an advanced constraints chapter (since we<br>
> don't have a QP solver available with MIP capability, unless I can<br>
> find time to do a chapter using CPLEX via PortfolioAnalytics via ROI).<br>
<br>
there is also a ROI front end to the MILP Symphony solver.<br>
<br>
I'm not sure iof the Symphony solver includes QP constraints.<br>
<br>
--<br>
Brian G. Peterson<br>
<a href="http://braverock.com/brian/" target="_blank">http://braverock.com/brian/</a><br>
Ph: <a href="tel:773-459-4973" value="+17734594973">773-459-4973</a><br>
IM: bgpbraverock<br>
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