Sensitivity analysis is the process of evaluating how the optimal solution in linear programming (LP) changes in response to variations in the problem's parameters, such as changes in the objective function's coefficients, the constraints' right-hand side, or the constraint coefficients. It assists decision-makers in determining if minor modifications to input values would affect the solution's viability or outcome, as well as how sensitive the ideal solution is to these changes. Sensitivity analysis is useful for assessing the solution's robustness and pinpointing crucial parameters or restrictions that have the biggest effects on the outcome.

Sensitivity analysis in linear programming (LP) is the study of how changes in the parameters of an optimization problem, such as objective function coefficients or constraint limits, affect the optimal solution and its feasibility.

Sensitivity analysis in linear programming (LP) is a technique used to examine how changes in input parameters (e.g., coefficients in the objective function, constraints) affect the optimal solution.

Sensitivity analysis in linear programming (LP) examines how changes in the parameters of a model, such as coefficients or constraints, affect the optimal solution. It helps identify the solution and the range of values for which the current optimal solution remains valid.

A method of discovering how the optimal solution is altered by changes, within certain ranges of the objective function coefficients and the right- hand side values.