Determines the solution of a linear programming problem. Details
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C is a vector describing the linear functional to maximize. |
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M is a matrix describing the different constraints. |
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B is a vector describing the right sides of the constraints inequalities. |
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maximum is the maximal value, if it exists, of x under the constraints. |
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X is the solution vector. |
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ticks is the time in milliseconds for the whole calculation. |
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error returns any error or warning condition from the VI. The nonexistence of a solution x leads to an error. |
The optimization problem cx = max! with the constraints x 0 and mx
b.
Here
and M a k by n matrix. Now you must decide whether or not an optimal vector x does exist, and if so, determine this vector x. The solution of a linear programming problem is a two-step process. The first step transforms the original problem into a problem in restricted normal form (essentially without inequalities in the formulation). The second step consists of the solution of this restricted normal form problem.
![]() | Note The previous formulation seems to be special. But there are many ways to reformulate terms. For instance, dx ![]() ![]() ![]() ![]() |