In a minimization problem, what does a positive reduced cost imply about a non-basic decision variable?
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In a minimization problem, a positive reduced cost means that increasing a non-basic decision variable from zero would increase the objective function value, so it would not improve the current solution.
In a linear programming minimization problem, a positive reduced cost indicates that forcing a non-basic decision variable into the optimal solution (increasing its value from zero) would increase the total objective function value by that reduced cost per unit, thereby making the solution worse (less optimal).
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