Prospects for Applied Mathematics and Data Analysis PAMDA 2836-4449 10.54216/PAMDA https://www.americaspg.com/journals/show/3872 2022 2022 Hessian matrix for testing the convexity and concavity of the objective function in nonlinear programming and neutrosophic nonlinear programming problems Faculty of Science, Damascus University, Damascus, Syria; Department of Requirements, International University for Science and Technology, Ghabageb, Syrian Arab Republic Maissam Maissam Mathematical examples rely on constructing mathematical models consisting of an objective function and constraints. These models may be linear, nonlinear, or otherwise. The objective function is either a maximization function or a minimization function for a given quantity. Nonlinear programming constitutes an important and fundamental part of operations research and is more comprehensive than linear programming. Therefore, researchers have focused on presenting studies that help find the optimal solution to these problems. Most of these studies have focused on the importance of knowing the type of objective function—whether it is convex or concave—because this knowledge helps determine the type of maximum value we obtain when studying a nonlinear programming problem. The Hessian matrix was used for this purpose. In this research, we will present the most important concepts that can be used when determining the type of maximum value for a nonlinear programming problem, as mentioned in some classic references. We will then reformulate them using the concepts of neutrosophic logic. 2024 2024 15 22 10.54216/PAMDA.040202 https://www.americaspg.com/articleinfo/34/show/3872