Lecture 23 | Descent, Backtracking & Unconstrained Minimization | Convex Optimization by Ahmad Bazzi

[@indraindisch]

Best convex optimization lectures on the internet !

[@Mutual_Information]

It's been so long since I've used MATLAB, but this reminds me how mature it is for optimization. Cool stuff! 👍

[@jefferyjamari6584]

Better than any of my uni professors 🙏🏻

[@thelinuxmann4659]

38:50 lovely illustration and amazing intuitive explanation of log barriers . Never would i have ever thought about arguing corner cases as you did. Respect.

[@halilibrahimkaradagl1117]

Perfect lecture professor.

[@sythe4112]

Trust me, no professor would give out intuitive explanations the way Ahmad does.

[@sukrudemirci546]

36:08 I can not believe log barrier minimization was that easy and I could not have understood it by myself. Ahmad is from another planet not from this earth. The argument he just used made me understand the entire log barrier minimization problem which I have never understood when studying. Ahmad I would love to donate without hesitating. Where can I do so ?

[@22flashback]

That was what I was thinking

[@joshlape6304]

explanation on KZbin.

[@thomasrahn7774]

@SIRAC you are right.

[@lalastoller2001]

How does he do that ?

[@zortistan9711]

Vandenbergh should see this

[@bruceysamo3553]

Yes, continue using MATLAB !

[@aishahoura2619]

00:00:00 Introduction 00:01:06 Unconstrained Minimization 00:01:36 Iterative Algorithm Assumptions 00:04:28 Gradient Equivalence 00:09:04 Unconstrained Least Squares 00:20:13 Unconstrained Geometric Program 00:28:10 Initial Subset Assumption 00:35:16 Intuitive Solution of Logarithmic Barrier Minimization 00:40:42 Generalization of Logarithmic Barriers 00:42:57 Descent Methods 00:50:42 Gradient Descent 00:52:59 Exact Line Search 00:56:23 Backtracking 01:00:25 MATLAB: Gradient Descent with Exact Line Search 01:17:35 MATLAB: Gradient Descent with Backtracking 01:20:12 MATLAB: Gradient Descent with Explicit Step Size Update 01:28:07 Summary 01:30:59 Outro

[@danmcgloven8169]

Back at convex optimization. Been waiting for this for a while now ! 👍

[@ozangumus433]

Cool content, just subbed !

[@rudolfsneller637]

This guy is super underrated by the KZbin algorithm.

[@alawimidher5899]

kudos for the detailed step by step explanation at 11:52 onward, it is always beneficial to have this type of quick recap, and it is a good practice when the teacher doesn't assume student mastery of certain topics that seem to be foundational.

[@ugurcanbayturk5261]

Please continue with your convex optimization lectures consistency. Love this one.

[@baturayanar9957]

Very underrated professor.

[@NDNGEmreOyunda]

A very wise man thanks so much Ahmad. Love the way you simplified log barrier optimization.

[@tektabanca3275]

Perfect lecture for those who want to understand algorithm used in convex optimization to minimize unconstrained problems. Job well done sir.

[@bereninrenklidunyas8683]

Ahmad , you are the best !

[@geraldcoachman6685]

God bless you Ahmad ❤️

[@beratcelik3791]

Perfect lesson !

[@bongainelsa5522]

Perfect lesson

[@victrgpena]

Thanks for the lesson 👍🏻

[@cecilyasmus7313]

helped a lot with discrete math in college!

[@genccocuk-bfk3459]

So much ads but you deserve it. I can not find this content elsewhere, this has a lot of research and a lot of sweat put in one video.

[@ayazmedya7571]

Great lecture love it all

[@orhansoyturk6789]

Thank you sir!! very helpful for my studies!!

[@AhmadBazzi]

Glad to hear that

[@susansletten8165]

YOU ARE THE BEST!

[@zoiladunford5759]

Ahmad thank you so much !

[@eneskasikara1576]

Perfect tutorial

[@rovsnyt4641]

When is next python lesson sir ?

[@pedadagopal3062]

please make the lectures for Equality constrained minimization and Interior-point methods

[@cuzwearespecial3379]

Excellent lessons!! Waiting for a video about Newton method etc :)

[@jameejeffcoat6834]

Never understood log barrier until I watched this lecture

[@chiqiu998]

Thanks for the excellent lecture;) Can you post videos regarding interior point method?

[@alawimidher5899]

AT 40:51 shouldn't f(x) be f(x)= - log(det( inv(F(x)) )) , did you miss the negative sign?

[@tieshagulbranson3815]

You should be at Stanford ahmad !

[@aracelymargit3962]

OMG I LOVE YOU

[@maxtrucks481]

🙏🏻

[@brujbruh2632]

👍🏻

[@mohammedbashamakh4690]

A lot of ads

[@sherlybattey7720]

👍🏻