Difficulty: Easy
Correct Answer: artificial variable
Explanation:
Introduction / Context:In linear programming, the simplex method often requires an initial basic feasible solution (BFS). When such a starting solution is not readily apparent from the constraints, the “two-phase” method or “Big-M” method introduces special variables to force feasibility temporarily. Understanding the purpose and nature of these variables is essential for interpreting algorithm steps correctly.
Given Data / Assumptions:
Concept / Approach:An artificial variable is appended to constraints to create an initial identity basis so that the simplex method can start. In Phase I of the two-phase method, the objective is to minimize the sum of artificial variables, driving them to zero and yielding a feasible solution in the original variables. If they cannot be driven to zero, the LP is infeasible. With the Big-M approach, large penalties in the objective discourage artificial variables from remaining positive in the optimal solution.
Step-by-Step Solution:
Identify the need: no obvious BFS from slack/surplus variables alone. Introduce artificial variables to form an initial basis. Use Phase I objective (minimize sum of artificial variables) to achieve feasibility. Proceed to Phase II (original objective) once artificial variables are eliminated.Verification / Alternative check:Standard LP textbooks describe artificial variables as computational devices without physical interpretation, introduced purely to obtain an initial BFS.
Why Other Options Are Wrong:
Common Pitfalls:Assigning a physical meaning to artificial variables; forgetting to remove them before interpreting the final solution.
Final Answer:artificial variable
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