Infeasibility in a linear programming problem can be detected by analyzing the final tableau of the Simplex Method, where positive artificial variables indicate conflicting constraints, or through graphical methods that show an empty feasible region. Both methods confirm the absence of viable solutions, signaling the need to reevaluate the problem constraints.

Infeasibility in a linear programming problem can be detected using several methods. The Simplex method reveals infeasibility if it fails to find an optimal solution in phase I. Graphically, if the feasible region formed by constraints does not overlap, the problem is infeasible. Analyzing constraints for conflicts that cannot be satisfied simultaneously also indicates infeasibility.

Infeasibility in an LP problem can be detected by checking if there is no common region that satisfies all the constraints, or by using methods like the **Simplex method** which will indicate infeasibility if no solution satisfies all constraints. Additionally, if the **dual variables** (in the dual problem) are negative for constraints that are supposed to be satisfied, it may also indicate infeasibility.