Advances on Mathematical Modeling and Optimization with Its Applications

<p><b>Advances on Mathematical Modeling and Optimization with Its Applications</b> discusses optimization equality and inequality constraints and their application in the versatile optimizing domain. It further covers non-linear optimization methods such as global optimization and gradient-based non-linear optimization and their applications.</p><ul> <li>Discusses important topics including multi-component differential equations geometric partial differential equations and computational neural systems</li> <li>Covers linear integer programming and network design problems along with an application of the mixed integer problems</li> <li>Discusses constrained and unconstrained optimization equality and inequality constraints and their application in the versatile optimizing domain</li> <li>Elucidates the application of statistical models probability models and transfer learning concepts</li> <li>Showcases the importance of multi-attribute decision modeling in the domain of image processing and soft computing</li> </ul><p>The text is primarily for senior undergraduate and graduate students and academic researchers in the fields of mathematics statistics and computer science.</p>