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Linear programming techniques will always produce an optimal solution to an LP problem.

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False

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A maximization problem is limited by all greater than or equal to constraints.

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True False

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False

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The term range of feasibility refers to a constraint's right-hand-side quantity.

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The simplex method is a general-purpose LP algorithm that can be used for solving only problems with more than six variables.

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Profit maximization could be an objective of an LP problem; but cost minimization cannot be the objective of an LP problem.

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The feasible solution space only contains points that satisfy all constraints.

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The term range of feasibility refers to coefficients of the objective function.

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If a single optimal solution exists to a graphical LP problem, it will exist at a corner point.

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The value of an objective function always decreases as it is moved away from the origin.

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The term isoprofit line means that all points on the line will yield the same profit.

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The feasible solution space is the set of all feasible combinations of decision variables as defined by only binding constraints.

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Graphical linear programming can handle problems that involve any number of decision variables.

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A change in the value of an objective function coefficient does not change the optimal solution.

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A shadow price indicates how much a one-unit decrease/increase in the right-hand-side value of a constraint will decrease/increase the optimal value of the objective function.

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