| algorithm | a procedure that always results in an optimum solution if the input data conform to the requirements for a mathematical model of this class.
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| binding constraint | a constraint which forms the optimal corner point of the feasible solution space.
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| constraint | a linear function of the decision variables that defines boundaries for the feasible space; a constraint could be an inequality of either the less-than-or-equal-to (<) or the greater-than-or equal-to (>) type, or else an equality (=).
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| continuous variable | a variable that is not restricted to integer or discrete values.
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| corner point | a corner point of the feasible space occurs whenever two linear constraints intersect on the boundary of the feasible region.
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| decision variable | a variable whose value can be set by a decision maker.
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| feasible solution space | the set of all feasible combinations of decision variables as defined by the constraints.
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| graphical linear programming | a graphical method for finding optimal solutions to two-variable problems.
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| heuristic | a procedure for solving a mathematical model that is not always guaranteed to result in the best possible solution.
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| linear function | a mathematical function of the form m = ax + by + cz + ..., where a, b, c, etc. are constants, x, y. z, etc. are variables; and m could be either a constant or a variable.
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| linear programming (LP) | an procedure for finding the values of decision variables, that results in the best solution of an optimization problem with linear constraints and a linear objective function.
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| maximization | a requirement that at the optimum, the objective function shall be at its highest value, within the feasible space.
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| minimization | a requirement that at the optimum, the objective function shall be at its lowest possible value, within the feasible space.
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| nonnegativity | all decision variables and all slack variables are required to be > 0.
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| objective function | a linear function of the decision variables, that is either maximized or minimized by the LP solution.
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| optimal solution | the best feasible solution, i. e., the values of the decision (and slack or surplus) variables that either maximize or minimize the value of the objective function.
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| parameters | constants in an equation.
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| range of feasibility | the range over which the righthand side value of a constraint can change without changing its shadow price.
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| range of optimality | the range over which the value of an objective function coefficient can change and not change the optimal solution.
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| redundant constraint | a constraint that does not form a boundary of the feasible solution space.
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| sensitivity analysis | an extension of simplex used to assess the impact of a change in the value of an objective function coefficient or a change in the right-hand-side value of a constraint.
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| shadow price | for a constraint, the amount that the value of the objective function would change if the RHS value of the constraint was changed by one unit.
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| simplex method | the algorithmic procedure for finding the optimal solution of an LP problem.
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| simultaneous solution | finding the coordinates of the point at which two straight lines intersect.
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| slack | when the values of decision variables are substituted into a constraint and the resulting value is less than the righthand side.
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| solution | a set of decision variables, and their respective values.
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| surplus | when the values of decision variables are substituted into a constraint and the resulting value is greater than the righthand side.
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