Graph Theory with Algorithms and its Applications: In Applied Science and Technology By Santanu Saha Ray English | EPUB | 2013 | 216 Pages | ISBN : 8132207491 | 4.97 MB
The book has many important features which make it suitable for both undergraduate and postgraduate students in various branches of engineering and general and applied sciences. The important topics interrelating Mathematics & Computer Science are also covered briefly. The book is useful to readers with a wide range of backgrounds including Mathematics, Computer Science/Computer Applications and Operational Research.
Working with Graph Algorithms in Python MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours | 219 MB Genre: eLearning | Language: English
This course focuses on how to represent a graph using three common classes of graph algorithms - the topological sort to sort vertices by precedence relationships, the shortest path algorithm, and finally the spanning tree algorithms.
Sanguthevar Rajasekaran, John Reif, "Handbook of Parallel Computing: Models, Algorithms and Applications (Chapman & Hall/CRC Computer and Information Science Series) " English | 2007 | ISBN: 1584886234 | 1224 pages | PDF | 10.5 MB
Henry Wolkowicz, Romesh Saigal, Lieven Vandenberghe, "Handbook of Semidefinite Programming: Theory, Algorithms, and Applications (International Series in Operations Research & Management Science)" English | 2000 | ISBN: 0792377710, 1461369703 | 654 pages | PDF | 3 MB
Algorithms for Computational Biology: 4th International Conference, AlCoB 2017, Aveiro, Portugal, June 5-6, 2017, Proceedings by Daniel Figueiredo English | 2017 | ISBN: 3319581627, 9783319581620 | 181 Pages | PDF | 8.92 MB
Algorithms for Data Science By Brian Steele, John Chandler, Swarna Reddy English | PDF,EPUB | 2016 | 438 Pages | ISBN : 3319457950 | 10.7 MB
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification.