May 1, 2024
3 minute read
QR Decomposition is a fundamental technique in linear algebra that has wide applications in various fields of science and engineering. It involves decomposing a matrix into the product of two simpler matrices: a unitary matrix Q and an upper triangular matrix R. QR Decomposition provides valuable insights into the structure of a matrix and simplifies many matrix operations.
History and Development
QR Decomposition was first introduced by the mathematician John Todd in 1956. He developed the algorithm as a more efficient method for solving systems of linear equations. Since then, QR Decomposition has been extensively studied and refined, and various algorithms have been developed to perform the decomposition.
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Find a path to becoming a QR Decomposition. Learn more at:
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Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
QR Decomposition.
Covers a wide range of matrix computations, including QR decomposition. It classic textbook that is suitable for both undergraduate and graduate students.
Provides a comprehensive treatment of numerical linear algebra, including QR decomposition. It valuable resource for researchers and practitioners.
Provides a comprehensive treatment of numerical methods for linear algebra, including QR decomposition. It valuable resource for researchers and practitioners.
Provides a comprehensive treatment of matrix analysis, including QR decomposition. It valuable resource for researchers and practitioners.
Provides a comprehensive treatment of matrix theory and applications, including QR decomposition. It valuable resource for researchers and practitioners.
Provides a comprehensive treatment of linear algebra, including QR decomposition. It popular textbook that is suitable for both undergraduate and graduate students.
Provides a comprehensive treatment of iterative methods for linear and nonlinear equations, including QR decomposition. It valuable resource for researchers and practitioners.
Provides a comprehensive treatment of matrix computations using MATLAB, including QR decomposition. It valuable resource for researchers and practitioners who use MATLAB.
Provides a comprehensive treatment of applied matrix algebra, including QR decomposition. It valuable resource for researchers and practitioners.
Provides a basic introduction to linear algebra, including QR decomposition. It suitable textbook for undergraduate students.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/e2nzqr/qr
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