What is the role of surplus variables in LP?
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Surplus variables are used to convert inequality constraints (specifically, greater-than-or-equal-to constraints) into equalities in LP problems. They represent the amount by which the left-hand side of a constraint exceeds the right-hand side.
Surplus variables in linear programming transform "greater than or equal to" constraints into equalities, enabling the use of the simplex method. They account for excess amounts in resource constraints and are essential for maintaining the model's feasibility while facilitating the optimization process.
Surplus variables in Linear Programming are used to convert inequalities into equalities, specifically in less-than-or-equal-to (≤) constraints. When a constraint is represented as an equation, a surplus variable is subtracted from the left side to account for the difference between the left and right sides of the inequality. This allows the problem to be solved using methods like the Simplex Method while maintaining the same feasible region and optimal solution.