Discrete Mathematics Optimization Techniques Mathematics Book B Sc 1st Sem Uor
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Discrete Mathematics & Optimization Techniques (Mathematics Book): B.Sc. 1st Sem UOR
Author: Dr. Prashant Chauhan
language: en
Publisher: Thakur Publication Private Limited
Release Date: 2023-12-01
Explore the e-book edition of "Mathematics (Discrete Mathematics & Optimization Techniques)" in English for B.Sc, First Semester, tailored for the Three/Four Year Undergraduate Programme, aligning with the University of Rajasthan, Jaipur Syllabus as per NEP (2020). Published by Thakur Publication, this comprehensive resource covers essential topics in discrete mathematics and optimization techniques, providing students with the necessary foundation for their academic journey.
Convex Optimization
Author: Stephen P. Boyd
language: en
Publisher: Cambridge University Press
Release Date: 2004-03-08
Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
Mathematics for Machine Learning
Author: Marc Peter Deisenroth
language: en
Publisher: Cambridge University Press
Release Date: 2020-04-23
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.