<div class="gmail_quote">Hello.<div><br></div><div>I'd like to know if anyone has some suggestions on how to improve solving performance for our case, for which we're currently using CLP.</div><div><br></div><div>We're trying to solve tens to hundreds of thousands of dynamically generated LPs, ranging from 30 variables by 500 constraints to a maximum of 400 variables by 100 000 constraints. Each new generated LP is a small modification from the one previously solved, where these modifications are always the addition or removal of constraints (the number of variables remains always the same) by using methods "addRow" and "deleteRows" on the existing model.</div>
<div><br></div><div>I've already played a little bit with some of the parameters I've found, and so far I discovered that by disabling presolve and scaling we improve a little our times.</div><div><br></div><div>
So, does anyone have any ideas on how to get even better results? Are there better ways of keeping this ever changing model? What other parameters should I play with?</div>
<div><br></div><div>Or even, if another project from COIN-OR would be more suitable for our case. Recently I read about DyLP: would it be an interesting option for this case?</div><div><br></div><div><br></div><div>Thank you for your time! Any help will be much appreciated.</div>
<div><br></div><div>Regards,</div><div>--<br>Carlos E. Knippschild</div></div>