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Full Description
Advances on Mathematical Modeling and Optimization with Its Applications 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.
Discusses important topics including multi-component differential equations, geometric partial differential equations, and computational neural systems
Covers linear integer programming and network design problems, along with an application of the mixed integer problems
Discusses constrained and unconstrained optimization, equality, and inequality constraints, and their application in the versatile optimizing domain
Elucidates the application of statistical models, probability models, and transfer learning concepts
Showcases the importance of multi-attribute decision modeling in the domain of image processing and soft computing
The text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science.
Contents
1. Mathematical Modeling and Technology. 2. Modeling with Discrete Dynamical Systems. 3. Differential Equations in the Modeling Perception. 4. Regression Methods and Models. 5. Linear Integer Programming. 6. Mixed Integer Programming. 7. Single Variable and Multivariable Optimization. 8. Nonlinear Optimization Methods. 9. Simulation Models. 10. Model based on the Decision Making. 11. Multi-Attribute Decision Modeling. 12. Mathematical Models in Machine Learning. 13. Application of Mathematical Models in the Deep Learning Model. 14. Optimization in the Machine Learning Models.
