Algorithms Analysis Practice Test 2025 - Free Algorithms Practice Questions and Study Guide

Linear programming is a mathematical method used for optimizing a linear objective function, subject to linear equality and inequality constraints. The inherent assumption behind linear programming is that both the objective function and the constraints can be represented as linear equations.

Therefore, when the objective function is curvilinear (i.e., not a straight line—such as quadratic, exponential, or any other non-linear functions), standard linear programming techniques are not applicable. This is because linear programming algorithms, like the Simplex method, rely on the properties of linear equations; they do not account for the complexities introduced by nonlinear functions.

As a result, linear programming is not effective for optimization problems characterized by curvilinear objectives, making the statement that it is effective in such cases false. Nonlinear programming would be the appropriate methodology to handle optimization problems where the objective function or constraints involve non-linear forms.