Awesome series....Very timely...Other series on convex programming or optimization are so lengthy and talk about relevant + irrelevant materials that one loose interest. For a Ph.D. student timing is a constraint and it helped a lot. In more or less half hour, he explains the concepts in detailed manner...I suggest one should listen to it on 1.25 times speed to save more time... Awesome
yupp its concise and highly informative.... thanks to guys @ nptel...this course seriously increase my respect towards to quality of nptel and yes watch it it higher speed (if you can handle it xD) and all the best for your phd man :D
This whole series is so amazing the way you teach it's the best. The only issue is the cameraman the way the person was always focusing out in super disturbing. But the video and your teaching is just the best
The only issue is the cameraman the way the person was always focusing out in super disturbing. exactly why would the cameraman do that unnecessarily 😫😫
the best lecture for KKT condition it covers all point for KKT condition.
Sir, the way you explained is beyond the excellent.
This helped me a lot. Thank you for sharing the lectures with the world :)
Awesome series....Very timely...Other series on convex programming or optimization are so lengthy and talk about relevant + irrelevant materials that one loose interest. For a Ph.D. student timing is a constraint and it helped a lot. In more or less half hour, he explains the concepts in detailed manner...I suggest one should listen to it on 1.25 times speed to save more time... Awesome
yupp its concise and highly informative.... thanks to guys @ nptel...this course seriously increase my respect towards to quality of nptel and yes watch it it higher speed (if you can handle it xD) and all the best for your phd man :D
totally agree
Very good way of teaching Sir. Thank you so much.
This whole series is so amazing the way you teach it's the best.
The only issue is the cameraman the way the person was always focusing out in super disturbing. But the video and your teaching is just the best
My man, you saved my life all the way back in 2018.
How to solve if one of the constraint is a non convex function ?
Fabulous job...thanks
Thank you, very excellent!
This is excellent!
Thank you for this great lecture professor. But you should have talked about regularity, shouldnt you have?
Excellent. Thanks
shouldn't lamda 2= 4 at 13:33?, somebody explain
Are there for alpha in the lagrange function always two solutions for alpha =0 and alpha bigger 0?
Sir why in convex programming problem all the constraints are less or equal type?
Superb!
The only issue is the cameraman the way the person was always focusing out in super disturbing. exactly why would the cameraman do that unnecessarily 😫😫
what about max f(x) ? is it different from this method ?
Great explanation Sir...Can you suggest a textbook or reference book for convex programming problems?
How to use this model in analysis by using any software. Please make video on this
very very good!
This is very resourceful
Awesome
29:00 -> ⲗ(X1^2+X2^2-4) = 0
Perfect.
Too good