Redundancy in linear programming can be detected by analyzing constraints to see if any do not affect the feasible region or optimal solution. This can be done through graphical methods for smaller problems or computational techniques for larger ones, comparing solutions with and without the suspected redundant constraints. Recognizing redundancy helps simplify models and improve efficiency.

Redundancy in LP can be detected by checking if a constraint does not alter the feasible region, using graphical analysis, or through computational methods like the Simplex method to identify constraints that don't impact the optimal solution.

Redundancy in LP can be detected by checking if a constraint does not affect the feasible region or optimal solution, often through methods like graphical analysis, the Simplex method, or evaluating the dual variables and matrix rank.

Redundancy can be detected by comparing constraints. If a constraint is a linear combination of other constraints or does not affect the feasible region, it is redundant.