Linear algebra

Orthogonalization

In linear algebra, orthogonalization is the process of finding a set of orthogonal vectors that span a particular subspace. Formally, starting with a linearly independent set of vectors {v1, ... , vk} in an inner product space (most commonly the Euclidean space Rn), orthogonalization results in a set of orthogonal vectors {u1, ... , uk} that generate the same subspace as the vectors v1, ... , vk. Every vector in the new set is orthogonal to every other vector in the new set; and the new set and the old set have the same linear span. In addition, if we want the resulting vectors to all be unit vectors, then we normalize each vector and the procedure is called orthonormalization. Orthogonalization is also possible with respect to any symmetric bilinear form (not necessarily an inner product, not necessarily over real numbers), but standard algorithms may encounter division by zero in this more general setting. (Wikipedia).

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11H Orthogonal Projection of a Vector

The orthogonal projection of one vector along another.

From playlist Linear Algebra

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11J Orthogonal Projection of a Vector

The orthogonal projection of one vector along another.

From playlist Linear Algebra

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11I Orthogonal Projection of a Vector

The Orthogonal Projection of one vector along another.

From playlist Linear Algebra

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Orthogonal Projections

In this video, I define the concept of orthogonal projection of a vector on a line (and on more general subspaces), derive a very nice formula for it, and show why orthogonal projections are so useful. You might even see the hugging formula again. Enjoy! This is the second part of the ort

From playlist Orthogonality

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Orthogonal sets

This is the first video of a linear algebra-series on orthogonality. In this video, I define the notion of orthogonal sets, then show that an orthogonal set without the 0 vector is linearly independent, and finally I show that it's easy to calculate the coordinates of a vector in terms of

From playlist Orthogonality

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Linear Algebra 7.1 Orthogonal Matrices

My notes are available at http://asherbroberts.com/ (so you can write along with me). Elementary Linear Algebra: Applications Version 12th Edition by Howard Anton, Chris Rorres, and Anton Kaul A. Roberts is supported in part by the grants NSF CAREER 1653602 and NSF DMS 2153803.

From playlist Linear Algebra

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The Diagonalization of Matrices

This video explains the process of diagonalization of a matrix.

From playlist The Diagonalization of Matrices

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The geometric view on orthogonal projections

Learning Objectives: 1) Given a vector, compute the orthogonal projection onto another vector This video is part of a Linear Algebra course taught by Dr. Trefor Bazett at the University of Cincinnati

From playlist Linear Algebra (Full Course)

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Orthogonal Complements

Orthogonal complements. The direct sum of a subspace and its orthogonal complement. Dimension of the orthogonal complement. The orthogonal complement of the orthogonal complement.

From playlist Linear Algebra Done Right

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Gram-Schmidt Orthogonalization for Three or More Vectors

https://bit.ly/PavelPatreon https://lem.ma/LA - Linear Algebra on Lemma http://bit.ly/ITCYTNew - Dr. Grinfeld's Tensor Calculus textbook https://lem.ma/prep - Complete SAT Math Prep

From playlist Part 4 Linear Algebra: Inner Products

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Orthogonal complement of the orthogonal complement | Linear Algebra | Khan Academy

Courses on Khan Academy are always 100% free. Start practicing—and saving your progress—now: https://www.khanacademy.org/math/linear-algebra/alternate-bases/othogonal-complements/v/lin-alg-orthogonal-complement-of-the-orthogonal-complement Finding that the orthogonal complement of the ort

From playlist Alternate coordinate systems (bases) | Linear Algebra | Khan Academy

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Mod-01 Lec-07 Cauchy Schwaz Inequality and Orthogonal Sets

Advanced Numerical Analysis by Prof. Sachin C. Patwardhan,Department of Chemical Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.ac.in

From playlist IIT Bombay: Advanced Numerical Analysis | CosmoLearning.org

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14. Orthogonal Vectors and Subspaces

MIT 18.06 Linear Algebra, Spring 2005 Instructor: Gilbert Strang View the complete course: http://ocw.mit.edu/18-06S05 YouTube Playlist: https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8 14. Orthogonal Vectors and Subspaces License: Creative Commons BY-NC-SA More information at ht

From playlist MIT 18.06 Linear Algebra, Spring 2005

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36 entangled officers of Euler: A quantum solution to a classically... by Arul Lakshminarayan

Colloquium: 36 entangled officers of Euler: A quantum solution to a classically impossible problem Speaker: Arul Lakshminarayan (IIT Madras, Chennai) Date: Mon, 06 June 2022, 15:30 to 17:00 Venue: Online and Madhava Lecture Hall Abstract The 36 officers problem of Euler is a well-known i

From playlist ICTS Colloquia

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P2L04_15Oct2020 (Tut6, Q6,11) (un-edited)

P2L04_15Oct2020: 4th lecture, zoom (un-edited), Tut 6, Q6 & 11 (recorded 15 Oct 2020) Quest 6) 23a 00:00 - 02:25 23b 02:25 - 08:15 23c 08:39 - 13:05 23d 13:05 - 16:30 23e 16:40 - 24:30 25:03 - 25:26 "How do we justify 23d" 24a 27:00 - 28:05 24b 28

From playlist Part 2 lectures (2020 zoom)

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3. Orthonormal Columns in Q Give Q'Q = I

MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k This lecture focus

From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018

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Linear Algebra - Lecture 38 - Orthogonal Sets

In this lecture, we discuss orthogonal sets of vectors. We also investigate the idea of an orthogonal basis, as well as orthogonal projections of vectors.

From playlist Linear Algebra Lectures

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Orthogonality and Orthonormality

We know that the word orthogonal is kind of like the word perpendicular. It implies that two vectors have an angle of ninety degrees or half pi radians between them. But this term means much more than this, as we can have orthogonal matrices, or entire subspaces that are orthogonal to one

From playlist Mathematics (All Of It)

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Linear Algebra 20j: The Dot Product, Matrix Multiplication, and the Magic of Orthogonal Matrices

https://bit.ly/PavelPatreon https://lem.ma/LA - Linear Algebra on Lemma http://bit.ly/ITCYTNew - Dr. Grinfeld's Tensor Calculus textbook https://lem.ma/prep - Complete SAT Math Prep

From playlist Part 3 Linear Algebra: Linear Transformations

Related pages

Gram–Schmidt process | Linear span | Biorthogonal system | Linear subspace | Unit vector | Linear algebra | Orthogonal basis | Singular value decomposition | Numerical stability | Arnoldi iteration | Division by zero | Iterative method | Householder transformation | Symmetric bilinear form | Real number | Euclidean space | Orthogonality | Projection (linear algebra) | Givens rotation | Inner product space | Reflection (mathematics)