{"id":43156,"date":"2026-07-02T07:35:49","date_gmt":"2026-07-02T07:35:49","guid":{"rendered":"https:\/\/uomosul.edu.iq\/en\/computerscience\/?p=43156"},"modified":"2026-07-02T07:35:49","modified_gmt":"2026-07-02T07:35:49","slug":"1-693","status":"publish","type":"post","link":"https:\/\/uomosul.edu.iq\/en\/computerscience\/2026\/07\/02\/1-693\/","title":{"rendered":"Doctorial thesis by &#8221; Abeer Abdulkhaleq Ahmad &#8221; Department of Mathematics"},"content":{"rendered":"<p>Discussion of the Doctorial thesis in the College of Computer Science and Mathematics -Department of (Department of Mathematics) entitled:<\/p>\n<p>\u201cA New Formula of Nonlinear Optimization Techniques with Application\u201d<\/p>\n<p><strong>supervised by \u00a0Prof. Dr. Huda I. Ahmed<\/strong><\/p>\n<p>The thesis addressed.<\/p>\n<p>This thesis aims the conjugate gradient method is one of the most prominent numerical methods used to solve nonlinear functions, especially those with large dimensions. It combines efficiency, rapid convergence, and simplicity of implementation, making it the preferred optimization method.<\/p>\n<p>This thesis focuses on developing the conjugate gradient method by deriving parameters to increase its numerical efficiency and convergence speed. A new conjugate coefficient was derived, and a spectral parameter was introduced using Andrei&#8217;s acceleration factor. Furthermore, a trinomial conjugate gradient algorithm was developed.<\/p>\n<p>Additionally, a trinomial conjugate gradient algorithm was developed to efficiently solve unconstrained optimization problems.<\/p>\n<p>All the proposed methods achieve sufficient regression and comprehensive convergence under specific conditions. In addition, these methods have been applied to several different standard test functions, demonstrating their efficiency and practical superiority. The Aquila optimization algorithm was also improved by introducing a new approach based on conjugate gradient algorithms, which utilizes method properties to extend the scope of the enclosure update. Statistical comparisons were used as a metric for the performance of the new algorithm.<\/p>\n<p>Finally, conclusions based on numerical and statistical comparisons were included, along with recommendations for future work.<\/p>\n<p>&nbsp;<\/p>\n<p>The discussion committee consists of<\/p>\n<ul>\n<li>Dr. Ban Ahmed Hassan (Chairman)<\/li>\n<li>Dr. Omar Saber Qassem (Member)<\/li>\n<li>Dr. Iman Tariq Hamed (Member)<\/li>\n<li>Dr. Nizar Khalaf Hussein (Member)<\/li>\n<li>Dr. Mona Mohsen Mohamed Ali (Member)<\/li>\n<li>Dr. Huda I. Ahmed (Member &amp; Supervisor)<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Discussion of the Doctorial thesis in the College of Computer Science and Mathematics -Department of (Department of Mathematics) entitled: \u201cA New Formula of Nonlinear Optimization Techniques with Application\u201d supervised by \u00a0Prof. Dr. Huda I. Ahmed The thesis addressed. This thesis aims the conjugate gradient method is one of the most prominent numerical methods used to solve nonlinear functions, especially those with large dimensions. It combines efficiency, rapid convergence, and simplicity of implementation, making it the preferred optimization method. This thesis focuses on developing the conjugate gradient method by deriving parameters to increase its numerical efficiency and convergence speed. A new conjugate <a href=\"https:\/\/uomosul.edu.iq\/en\/computerscience\/2026\/07\/02\/1-693\/\"> [Read More]<\/a><\/p>\n","protected":false},"author":24,"featured_media":43157,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-43156","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-viva"],"_links":{"self":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/43156","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/users\/24"}],"replies":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/comments?post=43156"}],"version-history":[{"count":1,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/43156\/revisions"}],"predecessor-version":[{"id":43158,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/43156\/revisions\/43158"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/media\/43157"}],"wp:attachment":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/media?parent=43156"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/categories?post=43156"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/tags?post=43156"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}