Modeling And Optimization Theory And Applications

Deep Learning and Neural Networks Theory and Applications with PyTorch! Including Transformers, BERT and GPT!

Deep Learning and Neural Networks Theory and Applications with PyTorch! Including Transformers, BERT and GPT!

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed.

by Athanasia Karakitsiou (Author)
Presents an up-to-date review of modeling and optimization approaches for location problems

Optimization: Structure and Applications presents selected contributions from renowned researchers in the fields of operations research and industrial engineering. The text is divided into two parts: the first focuses on mathematical structure, and the second, on real-world applications. The book includes recent developments in several optimization-related topics such as decision theory, linear programming, turnpike theory, duality theory, convex analysis, and queueing theory. The applications presented include, but are not limited to, data imaging, network capacity allocation, water system management, and materials design.