Question

how to prove for any linear transformation form $R_n$ to$R_m$ ,there is an orthonormal basis such that $T(u_i)*T(u_j) = 0$ if $i \neq j$

score 98 · Answer 1 · 2018-04-10 02:10:15

how to prove for any linear transformation form $R_n$ to$R_m$ ,there is an orthonormal basis such that $T(u_i)*T(u_j) = 0$ if $i \neq j$

score 38 · Answer 2 · 2026-04-05 22:49:16

38

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score 277 · Answer 3 · 2026-03-27 21:19:21

277

Views

Prove the $\ell^2$ norm of a linear transformation $A: \mathbb{R}^n \to \mathbb R^n$ is the maximum eigenvalue

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score 47 · Answer 4 · 2018-04-10 06:27:28

47

Views

A real symmetric matrix decomposed by diagonal entries

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score 47 · Answer 5 · 2026-03-24 19:08:44

47

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positive definite matrix as a matrix blocks

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115

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score 732 · Answer 7 · 2026-04-07 22:43:29

732

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Properties of $A^TA$ transformation

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score 1.1k · Answer 8 · 2026-04-06 21:36:51

1.1k

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score 250 · Answer 9 · 2026-03-25 19:03:34

250

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score 780 · Answer 10 · 2026-03-24 20:32:00

780

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score 106 · Answer 11 · 2026-03-25 03:02:16

106

Views

Prove that $\|\mathbf{X}\|_\sigma \leq \sqrt{rank(X)} \|\mathbf{X}\|_F$

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score 132 · Answer 12 · 2026-03-28 13:40:15

132

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score 1.6k · Answer 13 · 2026-03-27 14:55:11

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score 324 · Answer 14 · 2026-03-25 07:58:46

324

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score 116 · Answer 15 · 2026-04-07 19:37:43

116

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Published on 07 Apr 2026 - 19:37

List Question

how to prove for any linear transformation form $R_n$ to$R_m$ ,there is an orthonormal basis such that $T(u_i)*T(u_j) = 0$ if $i \neq j$

Solving for Unknown in Matrix

Prove the $\ell^2$ norm of a linear transformation $A: \mathbb{R}^n \to \mathbb R^n$ is the maximum eigenvalue

A real symmetric matrix decomposed by diagonal entries

positive definite matrix as a matrix blocks

Long-term probability of a Markov Chain with Absorbing States

Properties of $A^TA$ transformation

Find shape function for finite element

How to differentiate an expression with respect to a symmetric matrix?

Vector whose inner product is positive with every vector in given basis of $\mathbb{R}^n$

Prove that $\|\mathbf{X}\|_\sigma \leq \sqrt{rank(X)} \|\mathbf{X}\|_F$

Show that λ +c is an eigenvalue of B

Rank of Matrix and dimension of the image of the function corresponding with that matrix

eigenvector of compositions implies eigenvector of respective functions in composition?

Finding The Characteristic Polynomial of a Matrix With Integer Sum Coefficients.

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