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How to use or fill out the Simplex Method In Matrix Form (revised Simplex Method) A LP With N ... online
This guide provides clear instructions on how to fill out the Simplex Method In Matrix Form, also known as the revised Simplex Method, specifically tailored for optimizing linear programming problems. Follow these steps to gain clarity and confidence in using this method online.
Follow the steps to successfully complete the form.
- Click the 'Get Form' button to access the Simplex Method In Matrix Form document. This will allow you to open the form in your preferred editor.
- Begin by identifying the objective function you want to maximize or minimize, represented as 'max Z = cx', where 'c' is the coefficients vector and 'x' is the variables vector.
- Define the constraints of your problem in matrix form. This should be noted as 'Ax = b', ensuring that 'x' is non-negative (x ≥ 0).
- Input the coefficients for the objective function into the form, ensuring correct alignment with their respective variables. The format is a vector including all coefficients.
- Next, fill in the matrix A, which consists of the coefficients of the constraints. Each row should correspond to a different constraint.
- Specify the constants vector 'b' that corresponds to each equation in the constraints. Ensure the correct number of entries that matches the number of constraints.
- Calculate the initial basic feasible solution. Set the appropriate basic and non-basic variables based on the method’s requirements.
- Follow through the iterations of the simplex method, ensuring to compute values based on the matrices, including B and N matrices as necessary.
- Once the optimal solution is achieved, conclude by saving your input data. You can choose to download, print, or share the filled form for further analysis or submission.
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Revised simplex method is computationally more efficient and accurate. Duality of LP problem is a useful property that makes the problem easier in some cases and leads to dual simplex method. This is also helpful in sensitivity or post optimality analysis of decision variables.
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