Simplex is a method that is used in linear programming problems to obtain solutions for linear programming problems. A linear programming problem as a recursive involves determining the maximum or minimum value of an objective function given a set of constraints. Constraints will form the boundary of a polyhedron. Under the assumptions of the constraint set to convex any vertex in a polyhedron, the maximum or minimum of the objective function will produce an excessive value.

Having a vertex boundary, convex a vertex will yield a local minimum which is also a global minimum. Similarly in a concave function the local concave will also be the global maximum due to being concave. Re-joining the convex function is a function where a boundary of the function always falls within the connected line between any two points on the boundary of the function.

The simplex method begins by setting the value of the non-root variable to 0 and then proceeds to find the optimal value of the objective function by identifying the most significant gain or decrease of the value of the objective function. But Simplex assumes a starting point where non-root variables are set to 0. The optimal value of the objective function is found after several iterations where the algorithm selects a function that has the maximum gain of the absolute value of the objective function. The simplex method is efficient because it does not include all possible solutions, but converts to a real value in a small number of searches.

Here if the polyhedron has 4 or 5 vertices and the optimal solution is found after 5 iterations (for example), one must recognize that there is an implicit assumption that the first possible solution is determined by setting the non-basic variable to 0. Is done which is the coordinate of the (0,0) polyhedron.

It is noted here that by correcting non-basic variables as a starting point of the simplex to 0 one can assume a starting point that is far from optimal. So Simplex can be modified to make an intelligent impression about where iterations are required to start. The number of runs of the simplex is approximately proportional to the power of the number of constraints. One can apply some probabilistic methods to start the simplex at a point near the optimum and derive approximate rules.Simplex is a method that is used in linear programming problems to obtain solutions for linear programming problems. A linear programming problem as a recursive involves determining the maximum or minimum value of an objective function given a set of constraints. Constraints will form the boundary of a polyhedron. Under the assumptions of the constraint set to convex any vertex in a polyhedron, the maximum or minimum of the objective function will produce an excessive value.

Having a vertex boundary, convex a vertex will yield a local minimum which is also a global minimum. Similarly in a concave function the local concave will also be the global maximum due to being concave. Re-joining the convex function is a function where a boundary of the function always falls within the connected line between any two points on the boundary of the function.

The simplex method begins by setting the value of the non-root variable to 0 and then proceeds to find the optimal value of the objective function by identifying the most significant gain or decrease of the value of the objective function. But Simplex assumes a starting point where non-root variables are set to 0. The optimal value of the objective function is found after several iterations where the algorithm selects a function that has the maximum gain of the absolute value of the objective function. The simplex method is efficient because it does not include all possible solutions, but converts to a real value in a small number of searches.

Here if the polyhedron has 4 or 5 vertices and the optimal solution is found after 5 iterations (for example), one must recognize that there is an implicit assumption that the first possible solution is determined by setting the non-basic variable to 0. Is done which is the coordinate of the (0,0) polyhedron.

It is noted here that by correcting non-basic variables as a starting point of the simplex to 0 one can assume a starting point that is far from optimal. So Simplex can be modified to make an intelligent impression about where iterations are required to start. The number of runs of the simplex is approximately proportional to the power of the number of constraints. One can apply some probabilistic methods to start the simplex at a point near the optimum and derive approximate rules.