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>