Karush-Kuhn-Tucker Conditions (KKT) | Necessary and Sufficient Conditions

For the book, you may refer: amzn.to/3aT4ino
This lecture explains the Karush-Kuhn-Tucker Conditions (KKT) which are the Necessary and Sufficient Conditions for solving the NLPP.
Other lectures:
Examples of KKT Conditions with one Constraint: • KKT conditions with On...
KKT necessary & Sufficient Conditions: • Karush-Kuhn-Tucker Con...
Lagrange Multiplier method with Two constraints: • Lagrange Multiplier Me...
Lagrange Multiplier method with One constraint: • Lagrange Multiplier Me...
Maxima & Minima of the function: • Maxima and Minima of t...
Convex & Concave function: • Convex and Concave Fun...
Hessian Matrix: • Hessian Matrix of a fu...
Quadratic form: • Quadratic form with a ...

[@zubie2712]

great video sir, but can you speak a little louder and I bit slower sir please. its difficult to understand.

[@mymaths4063]

Once again great Video Professor 👌👌👌

[@DrHarishGarg]

Many thanks ... Keep watching

[@WattRosemary-i1t]

Davis Donna Robinson Susan Garcia Sharon

[@jatinsukhija289]

i think you didn't include slack variable in example

[@chandangupta9901]

Thank you sir...!!! It is really a very well organized video.

[@DrHarishGarg]

Thanks... Keep watching and sharing with other students too ..

[@tamimmehr2959]

Hello Dr. Harish Garg, Thank you from Germany and much respekt! You explained it very well

[@mulugetamulatu2053]

Thank you, from 🇪🇹

[@abditube0938]

Find the extreme of Lagrange multiplier Z=x^2 +2xy +yw^2 subject to 2x + y+w^2-24=0 and x+w-8=0 please Dr. help me

[@asifiqbalkhan5601]

Hello sir I have a doubt that value of lambda i >= 0 in both max and min problem in Most of the video and books but you said it depend on type of problem. Please clarify or give some reference plzzz I got very confuse after that about what is the difference in both.

[@Aestheticmonkeys]

Lots of love from tamilnadu sir , respect sir

[@rachnachauhan5562]

Thank u so much sir best video I have ever seen on this topic lots of respect

[@Steve6851-d6l]

Great video but please slow down a bit with the explanations. With the accent, i struggled to comprehend the explanations, and talking fast doesnt help.

[@aditya3984]

I think speed was alright. Accent might be a issue for most part.

[@aditya3984]

I think speed was alright. Accent might be a issue for most part.

[@IsheanesuMuchapireyi]

Where are the slack variables

[@nitinthakur4034]

It was really helpful sir ❤

[@PankajKumar-ot3mg]

Why you are not taking the non negativity constraints in lagrange function. My college teacher says by not taking non negativity constraints you are excluding the conditions for boundry solutins. Kindly explain

[@DrHarishGarg]

If non negativity conditions given in problem, then add one line during checking the solution... But no need to consider it in Lagrangian function.... If you consider x1,x2 as well in Lagrangian function then the function L has several variable and ultimately , you need to take alot of computational steps. ... .. Watch my next lecture on Examples of KKT, where these conditions are included.

[@imageprocessing9625]

Could you please help me with elephant herding algorithm

[@DrHarishGarg]

Will try

[@tadiosbelay2307]

nice presentation

[@SEHPATHIEDUCATION]

In nptel lectures lambda value is taken >0 for minimization problem.

[@DrHarishGarg]

Thanks.... Kindly check the expression of L too

[@SEHPATHIEDUCATION]

@@DrHarishGarg in one book by kalyanmoy deb ' L ' is same as you. Stil lembda is taken > 0 for minimization problem.

[@SEHPATHIEDUCATION]

Is lembda value not dependent on the type of constraint given in the problem.

[@DrHarishGarg]

Yes, of course lambda depends such as g(x)0

[@SEHPATHIEDUCATION]

@@DrHarishGarg but in video u told that lembda is less than zero for minimization problem whatever the constraint be

[@PankajKumar-ot3mg]

Sir but our teacher is saying not including the non negativity constraints in lagrange Will violate the kkt condition? Is that correct?

[@DrHarishGarg]

Kindly watch my video related to Examples of KKT, you will clear your doubt