Lecture 19 | Convex Optimization I (Stanford)

[@shiv093]

0:44 Feasibility and phase I methods(from lecture 18) 3:05 sum of infeasibilities phase I method 7:46 example 13:25 Complexity analysis via self-concordance 17:59 Newton iterations per centering step(total number) 27:28 polynomial-time complexity of barrier method 34:55 Generalized inequalities 39:25 properties 41:39 Logarithmic barrier and central path 45:23 example: semidefinite programming 46:48 Barrier method 48:19 Examples 52:48 family of SDPs 53:31 Primal-dual interior-point methods 58:03 Modeling

[@sakuag333]

Very dense and amazing course. Feeling glad that I could complete it. Learned a lot.

[@slippinchillin]

Absolutely excellent lectures, thank you Mr Boyd! Your lectures have given me tremendous insights in completing my final year project.

[@tag_of_frank]

Can't believe this has been on KZbin for 11 years already.

[@weirdman2822]

Feeling nostalgic after completing this last lecture.... Looking forward to 364B.......

[@martinspage]

fantastic lecture series. thank you so much!!!

[@zihaocheng6438]

This course is excellent, despite that I do not know how to apply this knowledge currently. I am sure it is the most useful course I ever learnt. Boyd is so great!

[@weihan3023]

convex xiao wang zi!

[@sridharthiagarajan479]

Great series, Boyd is god :)

[@saeedbonab4246]

Excellent lectures indeed! Anyone has an idea where could I find more about expressing models in convex optimization format (relaxation, sequential convex programming, simplifying models to be convex), few mentioned by prof at the end? I couldn't find related content in the book.

[@amirhosseinmoradian3739]

Thanks for sharing

[@annawilson3824]

14:08

[@vjpillay]

I think this professor should get some training from Prof G Strang of MIT how to lecture from the feet and taking the students as he walk and talk.