Simplex Method Of Solving Linear Programming Problem

Simplex Method Of Solving Linear Programming Problem-47
Let’s list some of the common pivot rules: It may happen that for some linear programs the simplex method cycles and theoretically, this is the only possibility of how it may fail.Such a situation is encountered very rarely in practice, if at all, and thus may implementations simply ignore the possibility of cycling.The solution of a linear program is accomplished in two steps.

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It may even happen that some tableau is repeated in a sequence of degenerate pivot steps.

It may even happen that some tableau is repeated in a sequence of degenerate pivot steps, and so the algorithm might pass through an infinite sequence of tableau without any progress. A pivot rule is a rule for selecting the entering variable if there are several possibilities, which is usually the case(in our algorithm determine this element).

There is a straightforward process to convert any linear program into one in standard form, so using this form of linear programs results in no loss of generality.

In geometric terms, the feasible region defined by all values of is a (possibly unbounded) convex polytope.

An extreme point or vertex of this polytope is known as basic feasible solution (BFS).

It can be shown that for a linear program in standard form, if the objective function has a maximum value on the feasible region, then it has this value on (at least) one of the extreme points.The simplicial cones in question are the corners (i.e., the neighborhoods of the vertices) of a geometric object called a polytope.The shape of this polytope is defined by the constraints applied to the objective function.The possible results from Phase II are either an optimum basic feasible solution or an infinite edge on which the objective function is unbounded above.George Dantzig worked on planning methods for the US Army Air Force during World War II using a desk calculator.In the previous part we implemented and tested the simplex method on a simple example, and it has executed without any problems. In the first part, we have seen an example of the unbounded linear program.What will happen if we apply the simplex algorithm for it?On this example, we can see that on first iteration objective function value made no gains.In general, there might be longer runs of degenerate pivot steps.The number of pivot steps needed for solving a linear program depends substantially on the pivot rule.The problem is, of course, that we do not know in advance which choices will be good in the long run.

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