Kuhn Tucker Optimality Conditions with inequality constraints. #KuhnTuckerConditions

preview_player
Показать описание
the Karush–Kuhn–Tucker (KKT) conditions, also known as the Kuhn–Tucker conditions, are first derivative tests (sometimes called first-order necessary conditions) for a solution in nonlinear programming to be optimal, provided that some regularity conditions are satisfied.

Allowing inequality constraints, the KKT approach to nonlinear programming generalizes the method of Lagrange multipliers, which allows only equality constraints. Similar to the Lagrange approach, the constrained maximization (minimization) problem is rewritten as a Lagrange function whose optimal point is a saddle point, i.e. a global maximum (minimum) over the domain of the choice variables and a global minimum (maximum) over the multipliers, which is why the Karush–Kuhn–Tucker theorem is sometimes referred to as the saddle-point theorem.
Consider the case of a two-good world where both goods, x and y. are rationed. Let the consumer’s utility function be U = U(x,y). The consumer has a fixed money budget of B and faces the money prices Px and Py. Further, the consumer has an allotment of coupons, denoted C, which can be used to purchase both x or y at a coupon price of cx and cy. Therefore the consumer’s maximization problem is Maximize U =U(x,y) Subject to and B ≥Pxx+Pyy C ≥cxx+cyy in addition, the non-negativity constraint x ≥ 0 and y ≥ 0. The Lagrangian for the problem is Z =U(x,y)+λ(B−Pxx−Pyy)+λ2(C −cxx+cyy) where λ,λ2 are the Lagrange multiplier on the budget and coupon constraints respectively. The Kuhn-Tucker conditions are Zx =Ux−λ1Px−λ2cx =0 Zy =Uy −λ1Py −λ2cy =0 Zλ1 = B−Pxx−Pyy ≥0 λ1≥0 Zλ2 = C −cxx−cyy ≥0 λ2≥0 Numerical Example Let’s suppose the utility function is of the form U = x · y2. Further, let B =100,Px = Py =1while C =120and cx =2,cy =1. The Lagrangian becomes Z =xy2+λ1(100−x−y)+λ2(120−2x−y) The Kuhn-Tucker conditions are now Zx =y2 −λ1−2λ2 ≤0 x≥0 x·Zx=0 Zy =2xy−λ1−λ2 ≤0 y ≥0 y·Zy =0 Zλ1 =100−x−y ≥0 λ1 ≥0 λ1·Zλ1 =0 Zλ2 =120−2x−y ≥0 λ2 ≥0 λ2·Zλ2 =0
Рекомендации по теме
Комментарии
Автор

Make sure you like comment and share this channel with your friends

ECONMATHS
Автор

Just dropped by to say you saved my life back in college. Indian guys on youtube are a blessing.

GretgorPooper
Автор

Such an amazing video! you explained it so much better than any other video I watched (and my teachers)!

---nurv
Автор

One of the best videos on KKT conditions. Thank you!!

tariqislam
Автор

The rooster is also learning the Karush–Kuhn–Tucker conditions :D

selamidurmus
Автор

Thank you so much for this video. It really helped me for our examination tomorrow.

ykdzzn
Автор

u rescued me from my math in econ exam. Thanks and please keep doing these videos ! They are really helpful

蒼井なつこ
Автор

this was so so helpful and clearly explained! thank you

mantrikashukla
Автор

This is gonna help IGNOU MEC students a lot

thotyashimray
Автор

Wonderful explanation resulting in wonderful understanding...
Thank you, sir...

laminbsanyang
Автор

Keep up the good work. The video content was very useful.

merin_here_am_i
Автор

best kuhn-tucker explainer video on YouTube!!!

greenfairyfloss
Автор

Thankkk you u helping me understand it very well 🙏🏻

kaylashevadena
Автор

This is amazing! Clearly and well explained.Thank you sir.

temamuna
Автор

Thank you very much
you have been very helpful

johnmefful
Автор

My professor need to subscribe to your channel man, I understood you better than the professor or even my friend who tried explaining it after

tjaloismtowa
Автор

sir, please make a video on projected gradient method and quadratic programming method

shubhranshuranjandas
Автор

Thank you so much... from Ethiopia!
Just one more thing, you also need to put both lambda 1 and 2 to be greater than zero, which means checking if both constraints work.

Thanks

mightym
Автор

So what are those 4 conditions? Please explain each

ArmanAli-wwml
Автор

Can you cover the principal agent problem in information economics

chrismashau