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Linear Algebra II | For the course MATH2102 and MATH1111 offered by Department of Mathematics of The University of Hong Kong (HKU)

For the course MATH2102 and MATH1111 offered by Department of Mathematics of The University of Hong Kong (HKU)

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Linear Algebra II | For the course MATH2102 and MATH1111 offered by Department of Mathematics of The University of Hong Kong (HKU) | hkumath1111.wordpress.com Reviews
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For the course MATH2102 and MATH1111 offered by Department of Mathematics of The University of Hong Kong (HKU)
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1 linear algebra ii
2 such that
3 are related by
4 how is
5 defined
6 where
7 wrt orthonormal bases
8 then
9 is symmetric
10 ie satisfying
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linear algebra ii,such that,are related by,how is,defined,where,wrt orthonormal bases,then,is symmetric,ie satisfying,are particularly nice,is similar to,next suppose,write,then from,we get,that means,assuming,if we set,the matrix,is diagonal,note that
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Linear Algebra II | For the course MATH2102 and MATH1111 offered by Department of Mathematics of The University of Hong Kong (HKU) | hkumath1111.wordpress.com Reviews

https://hkumath1111.wordpress.com

For the course MATH2102 and MATH1111 offered by Department of Mathematics of The University of Hong Kong (HKU)

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1

Lecture 25 | Linear Algebra II

https://hkumath1111.wordpress.com/2014/11/10/lecture-25-2

November 10, 2014. Filed under: 2014 Fall. 8212; Y.K. Lau @ 5:40 PM. We started inner product spaces and finished Section 10.1, covering some basic structures (see Lect25.pdf. More important (and deep) results, as listed below, will come up. Orthonormal basis and Gram-Schmidt process. Orthogonal complement and Projection. The adjoint of a linear operator (on inner product space). Orthogonal diagonalization and Principal axis theorem. Orthogonal similarity and Schur’s triangulation theorem.

2

Linear Transformation – A little overview | Linear Algebra II

https://hkumath1111.wordpress.com/2014/09/22/linear-transformation-a-little-overview

September 22, 2014. Linear Transformation – A little overview. Filed under: 2014 Fall. 8212; Y.K. Lau @ 7:24 PM. Today we start to study the linear transformation which is a big topic and an overview is given below. We have covered (1)-(4). The concepts of kernel. Of a linear transformation is a generalization of the nullspace. And the column space. Definition (and examples) of linear transformations. Induced by a matrix. Basic properties of a linear transformation (Thm 7.1.1). Of a linear transformation.

3

Lecture 23 | Linear Algebra II

https://hkumath1111.wordpress.com/2014/11/07/lecture-23-2

November 7, 2014. Filed under: 2014 Fall. 8212; Y.K. Lau @ 3:50 PM. Some remarks to supplement Friday’s lectures:. Has only 1 eigenvalue. And its geometric multiplicity is 1 (strictly less than its algebraic multiplicity 2), so. Indeed for this simple case, we may check that. Is not diagonalizable by brute-force. All cases are impossible as. Cayley-Hamilton’s Theorem says that for an. If its characteristic polynomial is. Some classmates asked if the characteristic equation. The answer is NO: it only means.

4

Lecture 22 | Linear Algebra II

https://hkumath1111.wordpress.com/2014/11/03/lecture-21-2

November 3, 2014. Filed under: 2014 Fall. 8212; Y.K. Lau @ 10:23 PM. A brief summary: Given a linear operator. On finite-dimensional vector space. For any ordered bases. Is an eigenvalue of. Is an eigenvalue of (any) matrix representation of. Wrt an ordered basis. Their eigenvectors are related via the coordinate isomorphism. (See p.458 in Lect21.pdf. Invariant subspace, then we are able to find a matrix representation in nice block form. (See p.455.). Leave a Comment ». Feed for comments on this post.

5

Lecture 16 | Linear Algebra II

https://hkumath1111.wordpress.com/2014/10/20/lecture-16-2

October 20, 2014. Filed under: 2014 Fall. 8212; Y.K. Lau @ 5:15 PM. Today we illustrated the use of the First Isomorphism Theorem, please see 1stFT(updated).pdf. Next we returned to Chapter 9. The motivation is based on. Induces a linear transformation. Can be represented by a matrix. In the sense that. Now we hope to extend the result to general vector spaces (i.e. to represent a general linear transformation by a matrix). The goal is clear and natural but how to do it is another matter. You are comment...

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Linear Algebra II | For the course MATH2102 and MATH1111 offered by Department of Mathematics of The University of Hong Kong (HKU)

November 20, 2014. Filed under: 2014 Fall. 8212; Y.K. Lau @ 8:59 PM. Today we learnt: Every linear operator. On inner product space has a “mate”. In today’s lecture we studied the definition of. Which is based on the important result: If. Is a linear transformation, then there exists unique. Is an orthonormal basis for. This result tells the relation between the matrix representations of. Below we shall see that self-adjoint operator. For some invertible matrix. For any ordered bases. Form a basis for.

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