9781032481104 - Advances on Mathematical Modeling and Optimization with Its Applications (13 risultati)

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Paperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New.

Paperback. Condizione: Brand New. 262 pages. 7.00x0.59x10.00 inches. In Stock.

Paperback. Condizione: new. Paperback. 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 systemsCovers linear integer programming and network design problems, along with an application of the mixed integer problemsDiscusses constrained and unconstrained optimization, equality, and inequality constraints, and their application in the versatile optimizing domainElucidates the application of statistical models, probability models, and transfer learning conceptsShowcases the importance of multi-attribute decision modeling in the domain of image processing and soft computingThe text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science. Discusses machine learning models and their optimization in mathematical modeling. Covers important topics such as linear integer programming, network design problems, mixed integer problems, constrained and unconstrained optimization, constrained integer programming, and gradient-based nonlinear optimization. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condizione: new. Paperback. 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 systemsCovers linear integer programming and network design problems, along with an application of the mixed integer problemsDiscusses constrained and unconstrained optimization, equality, and inequality constraints, and their application in the versatile optimizing domainElucidates the application of statistical models, probability models, and transfer learning conceptsShowcases the importance of multi-attribute decision modeling in the domain of image processing and soft computingThe text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science. Discusses machine learning models and their optimization in mathematical modeling. Covers important topics such as linear integer programming, network design problems, mixed integer problems, constrained and unconstrained optimization, constrained integer programming, and gradient-based nonlinear optimization. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr. Gunjan Mukherjee is currently an associate professor in the Department of Computational Sciences at Brainware University. He completed his BSc in Physics from Calcutta University, MCA from IGNOU, MTech from Calcutta University and ea.

Paperback. Condizione: new. Paperback. 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 systemsCovers linear integer programming and network design problems, along with an application of the mixed integer problemsDiscusses constrained and unconstrained optimization, equality, and inequality constraints, and their application in the versatile optimizing domainElucidates the application of statistical models, probability models, and transfer learning conceptsShowcases the importance of multi-attribute decision modeling in the domain of image processing and soft computingThe text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science. Discusses machine learning models and their optimization in mathematical modeling. Covers important topics such as linear integer programming, network design problems, mixed integer problems, constrained and unconstrained optimization, constrained integer programming, and gradient-based nonlinear optimization. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condizione: Neu. Advances on Mathematical Modeling and Optimization with Its Applications | Gunjan Mukherjee (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | CRC Press | EAN 9781032481104 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Discusses machine learning models and their optimization in mathematical modeling. Covers important topics such as linear integer programming, network design problems, mixed integer problems, constrained and unconstrained optimization, constrained integer programming, and gradient-based nonlinear optimization.