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  <doi_batch_id>aspg-34-3872-1792226099</doi_batch_id>
  <timestamp>20261017083459</timestamp>
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   <depositor_name>American Scientific Publishing Group</depositor_name>
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  <journal>
   <journal_metadata language="en">
    <full_title>Prospects for Applied Mathematics and Data Analysis</full_title>
    <abbrev_title>PAMDA</abbrev_title>
    <issn media_type="electronic">2836-4449</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>4</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Hessian matrix for testing the convexity and concavity of the objective function in nonlinear programming and neutrosophic nonlinear programming problems</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Maissam</given_name>
      <surname>Jdid</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Science, Damascus University, Damascus, Syria; Department of Requirements, International University for Science and Technology, Ghabageb, Syrian Arab Republic</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>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.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>09</first_page>
     <last_page>14</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3872</item_number>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/PAMDA.040202</doi>
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