Advanced Linear And Matrix Algebra by Nathaniel Johnston
- Publisher: MATHEMATICS
- Availability: In Stock
- SKU: 47022
- Number of Pages: 494
Rs.990.00
Rs.1,395.00
Tags: abstract linear algebra , advanced algebra concepts , Advanced Linear , advanced linear algebra , advanced matrix methods , algebra for computer science , applied linear algebra , bsc bs math book , eigenvalues and eigenvectors book , graduate algebra course book , graduate linear algebra book , higher mathematics book , linear algebra for physics , linear algebra self-study , linear algebra textbook pdf , linear maps and transformations , linear transformations textbook , math textbook for engineers , math theory reference , mathematical proofs algebra , mathematical structures book , Matrix Algebra , matrix algebra book , matrix decompositions , matrix theory advanced , modern algebra theory , Nathaniel Johnston , nathaniel johnston author , nathaniel johnston mathematics , numerical linear algebra , quantum computing algebra , textbook for math majors , university algebra guide , upper level linear algebra , vector spaces theory
Advanced Linear and Matrix Algebra
Author: Nathaniel Johnston
Binding: Paperback
Paper Quality: Yellow Paper
Category: Mathematics / Linear Algebra / Advanced Undergraduate & Graduate Textbook
Recommended For: Advanced undergraduate and graduate students in mathematics, computer science, and engineering; educators and researchers needing a rigorous algebra reference.
Key Points
-
Comprehensive Coverage – Explores core concepts of linear algebra with a strong focus on matrix theory and its applications.
-
Advanced Theoretical Focus – Ideal for readers with a solid background in linear algebra seeking deeper insight into vector spaces, transformations, and matrix decompositions.
-
Modern Mathematical Language – Presents rigorous proofs and definitions, aligning with modern approaches to mathematical writing and pedagogy.
-
Application-Oriented – Includes examples and problems relevant to fields like quantum information, optimization, and numerical analysis.
-
Excellent for Self-Study – Clear structure, logical progression, and challenging exercises make it suitable for independent learners as well.
════ ⋆★⋆ ═══
Writer ✤ Nathaniel Johnston