Lagrangean method second order conditions

[@sami-samim]

Thanks a lot for the video! It helped me a lot in understanding Hessian, minor principles and calculating second order derivatives to find critical point.

[@momodouyerrosallah239]

Thank you for making it simple for us 💕

[@fanz4088]

Thanks your post!!! In your example, you only have one constraint that x+y=5. What if you have more than one constraint, will the Modified Hessian Matrix still work? What will the expression of the matrix be like?

[@ainanaoroibam3375]

Thank you this video made me understand the SOC

[@pinky_ktheone8279]

u just saved my life

[@debtanudas2084]

Awesome explanation Sir! Thanks

[@liamwalker92]

Hi can you please clarify how you got Lxy I found that part a little hard to follow

[@gabrielr6803]

Aren't the g1 and g2 supposed to be negative? since they are technically the 2nd derivative of the Lagrangian with respect to lama and Xs? d^2L/d(lambda)d(x1)=L31=-g1?

[@shakibishfaq8627]

No.

[@shakibishfaq8627]

The derivative of the constraint is equal to the constraint, just change of sign, by bringing variables onto the left hand side.

[@333asg]

thanks a lot! fantastic explanation

[@lakshmimithranair2684]

Thank you.....well explained

[@katayamakinen]

Ok but why we have to take the bordered Hessian instead of getting the ordinary Hessian for F at its critical points (like an ordinary unconstrained problem)?Isn't that if the constrained F has some critical points we can tell what kind they are by just study the Hessian for F?

[@LavinaMadan]

When constraint is given, we have to take bordered hessian matrix.

[@shivanisingh3913]

Please let me the diffrence between hessian and bordered hessian matrix....

[@faridel-aouadi1776]

Hessian matrix is used when there are no constraints and the determinant of it can be used to deduce whether a particular point is a maxima or minima. The bordered hessian matrix is used when you have conditions to adhere to with your objective function. Hope that helps!

[@nakkulmahajan1170]

Thanks a lot you saved me

[@susanchang3388]

Awesome vid, helped me a lot!!!

[@lucykinnear9476]

this is great thank u so much !!!!!!

[@amiralayed8463]

Good luck

[@cvismenu]

Thank you

[@ratikantasahoo9539]

H1^b is 0

[@nayabgull7086]

nice