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In this video I have explained Separable Programming algorithm and question.
👉 Few questions covered:
1.what is Separable Programming ?
2.what is Separable functions ?
3.what is convex functions ?
4.what is convex programming ?
About Separable Programming:
Separable programming is deal with such non linear programming problems in which the objective function as well as all the constraints are separable. In the decisions making situations the non linear profit or cost functions are related by relatively smooth curves rather than sharp curves. Thus breaking point are obtained on such curves. In such cases the non linear objective function with a smooth curve for it , may be approximated by a series of piece wise linear segments. This approximation will introduce some error may be negligibly small in many cases. Such error can be reduced much by increasing the number of linear segments .Piece wise linear approximation can be done for convex as well concave functions.
Separable Programming problem: A NLPP in which the objective function can be expressed as a linear combination of several different single variable function of which some or all are non linear is called a separable programming problem.
Separable programming Algorithm:
step 1: If the objective function is of minimization form convert it into maximization.
step 2: Test whether the function fj(xj) and gij(xj) satisfy the concavity(convexity) conditions required for the maximization(minimization) of non linear programming problem. If conditions are not satisfied the method will not be applicable.
step 3: Divide the interval into number of mesh points.
step 4: For each mesh point compute the piece wise linear approximation.
step 5: Using the step 4 write the piece wise linear approximation of the given nlpp.
step 6: Now solve the resulting nlpp by simplex method .
Step 7:Finally we obtain the optimal solution xj of the original problem by using the relation.
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