Kuhn Tucker Method |Karush khun Tucker conditions Quadratic Programing | NLPP in English

kuhn tucker conditions with solved problem in hindi - Quadratic Programming Problem
Kuhn Tucker necessary and sufficient conditions with solved problem
kuhn tucker in nlpp

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About Quadratic Programming Problem and Kuhn Tucker conditions:
Quadratic Programming :A nlpp with non linear objective function and linear constraints such nlpp is called quadratic programming problem .
NLPP : The term non linear programming usually refers to the problem in which the objective function becomes non linear or one or more of the constraints inequalities have non linear relationship or both .
It means objective function may be non linear but the set of constraints may be linear or non linear .
Kuhn Tucker necessary and sufficient conditions :
The necessary and sufficient conditions kuhn tucker conditions to get an optimal solution to the problem of maximizing the given quadratic objective function subject to linear constraints .
consider the general form of nlpp which is having a maximization objective function with all less than or equal to type constraints.
Max z = f(x1,x2.....xn)
subject to
G(x1,x2.....xn)(less than equal to ) bi
xj is non negative.
Step 1: Convert inequality in to equation by introducing slack variables
Step 2: Construct lagrangian function
Step 3: Find the first partial derivative of lagrangian function with respect of all variable used in quadratic problem
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