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Simplex (quick recap)

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Replace all the inequality constraints by equalities, using slack variables

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8 constraints 5 variables (i.e., 5 dimensional space) any vertex solution corresponds to the intersection of 5 tight constraints. 3 of these are three equalities. other 2 must be xi = 0 and xj = 0 for some i,j x2x1 x3 x4 x5 (x1 = 0, x2 = 0, x3 = 1, x4 = 3, x5 = 2)

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non-basic vars basic vars non-basic vars are set to 0. basic vars values are then fixed Pick a non-basic variable (say x2) with positive coefficient in objective increase its value until some other variable (x3) goes to 0 x2 enters basis, x3 exits basis. x2x1 x3 x4 x5 (x1 = 0, x2 = 0, x3 = 1, x4 = 3, x5 = 2) constraint for x3 now tight constraint for x2 no longer tight which one? (pivot rule)

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non-basic vars are set to 0. basic vars values are then fixed (x1 = 0, x2 = 1, x3 = 0, x4 = 3, x5 = 1) x2x1 x3 x4 x5 Pick a non-basic variable (say x2) with positive coefficient in objective increase its value until some other variable (x3) goes to 0 x2 enters basis, x3 exits basis. constraint for x3 >= 0 now tight constraint x2 >= 0 no longer tight

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non-basic vars are set to 0. basic vars values are then fixed Pick a non-basic variable (say x1) with positive coefficient in objective increase its value until some other variable (x5) goes to 0 x1 enters basis, x5 exits basis. (x1 = 0, x2 = 1, x3 = 0, x4 = 3, x5 = 1) x2x1 x3 x4 x5

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non-basic vars are set to 0. basic vars values are then fixed Pick a non-basic variable (say x3) with positive coefficient in objective increase its value until some other variable (x4) goes to 0 x3 enters basis, x4 exits basis. (x1 = 1, x2 = 2, x3 = 0, x4 = 2, x5 = 0) x2x1 x3 x4 x5

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non-basic vars are set to 0. basic vars values are then fixed Pick a non-basic variable with positive coefficient in objective if no such variable, must be at optimum. (x1 = 3, x2 = 2, x3 = 2, x4 = 0, x5 = 0) x2x1 x3 x4 x5

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Pick a non-basic variable (say x2) with positive coefficient in objective non-basic vars are set to 0. basic vars values are then fixed If it can be increased arbitrarily, LP is unbounded

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Pick a non-basic variable (say x2) with positive coefficient in objective non-basic vars are set to 0. basic vars values are then fixed If it can be increased by only zero, degenerate vertex.

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Pick a non-basic variable (say x1) with positive coefficient in objective non-basic vars are set to 0. basic vars values are then fixed now it can be increased… What if we keep cycling in this degenerate vertices (or degenerate faces)? Solution: anti-cycling pivot rules.

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Pivot rules largest coefficient largest increase steepest edge Bland’s rule (non-cycling) Random edge often best in practice

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SIMPLEX METHOD FOR LP LP Model.

SIMPLEX METHOD FOR LP LP Model.

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