<div dir="ltr"><div dir="ltr">Hey Dr. Schmitz,<div><br></div><div>For the first point, yes that is what I mean, First we'll reformulate the problem to unconstrained, then we will use an existing algorithm.</div><div><br></div><div>For the second point, I was thinking of generating 50 random points using the user defined bounds, then substitute in the objective, and choose the initial that minimizes our goal.</div><div><br></div><div>Thanks,</div><div>Mazen</div><div><br><div><br><br></div></div></div><br><img width="0" height="0" class="mailtrack-img" alt="" style="display:flex" src="https://mailtrack.io/trace/mail/3c133165a2deadb91e1759b714986f28d55d3b4e.png?u=6962871"><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sun, Apr 11, 2021 at 3:09 PM Daniel Schmitz <<a href="mailto:danielschmitzsiegen@googlemail.com">danielschmitzsiegen@googlemail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div>Hey Sayed,</div><div><br></div><div>my two cents, not being a CoreDeveloper but a python developer interested in Optimization algorithms. <br></div><div><br></div><div>The automatic reformulation of the constrained problem into an unconstrained problem sounds similar to nlopt's augmented lagrangian: <a href="https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#augmented-lagrangian-algorithm" target="_blank">https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#augmented-lagrangian-algorithm</a> . I think this would be a great addition to scipy.optimize. I imagine that you would pass the reformulated objective to minimize then and just reuse the existing algorithms. <br></div><div><br></div><div>One objection to your idea about "smart initialization": why exactly 50 points and how exactly would they be sampled if no bounds are provided? Theoretically, a grid search over samples generated by for example latin hypercube sampling within a bounded volume could be a better initialization than a random guess. But I am not sure that this is in many cases a good idea. If you have no idea how to initialize your optimizer, I would go for one of the global optimizers.</div><div><br></div><div>Best,</div><div><br></div><div>Daniel<br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sun, 11 Apr 2021 at 14:41, Mazen Sayed <<a href="mailto:sayedmazen70@gmail.com" target="_blank">sayedmazen70@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div>Dear,<div><br></div><div>I hope this email finds you well, this is my proposal for scipy.optimize project, I'm really interested to work on this project.</div><div><br></div><div>Thanks</div></div><div><br></div><a href="https://drive.google.com/file/d/12Q6NnorN74VkuQw_HRx2kuY-FIoK90V0/view?usp=sharing" target="_blank">https://drive.google.com/file/d/12Q6NnorN74VkuQw_HRx2kuY-FIoK90V0/view?usp=sharing</a><br><br><br><br><img alt="" style="display: flex;" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" width="0" height="0"></div><div id="gmail-m_7582723163811328871gmail-m_6919920764018287639DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2"><br>
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