Linear programming, while a powerful tool in operations research, has certain limitations that can impact its applicability and effectiveness in solving complex real-world problems. Here are some limitations of linear programming:

1. Linearity Assumption: Linear programming assumes that relationships between variables are linear, which might not hold true for all problems. Nonlinear relationships can lead to inaccurate solutions.

2. Simplistic Modeling: Linear programming might oversimplify complex real-world situations, neglecting factors like uncertainty, risk, and dynamic changes in variables over time.

3. Limited Variables and Constraints: Linear programming becomes challenging when dealing with a large number of variables and constraints. Complex problems might require too many resources to solve optimally.

4. Integer Solutions: Integer linear programming (ILP) deals with discrete decision variables, which can lead to computational complexity and intractability for larger problems.

5. Sensitivity to Input Parameters: Small changes in input parameters can lead to significant changes in the optimal solution. This sensitivity can be problematic when input data is subject to inaccuracies.

Limited scope

Sensitivity to errors

Assumption of linearity

Dependence on accurate data

Even after determining a specific function, it is difficult to describe the limitations.

In cases where solutions must be integers (e.g., the number of items to produce or the number of employees to hire), linear programming might not provide feasible integer solutions. 

Sensitivity to Small Changes it is because linear programming solutions can be highly sensitive to small changes in coefficients of the objective function or constraints.