Norm is a function on a vector space $X$ which generalizes notion of length of vector in general vector spaces.

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Why one would want to normalize a matrix by dividing it by its Frobenius norm?

I am currently reading a scientific paper about clustering of brain signals, which consist on long time series across many channels (each signal is a matrix of C channels by T time samples). In the ...
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Do real vectors attain matrix norms?

The $p$-norm of a matrix $A$ for $p \geq 1$ is defined as $$\|A\|_p = \max_{ x \in \mathbb{R}^n, \|x\|_p=1} \|Ax\|_p.$$ My question: does this equal $$ \max_{ x \in \mathbb{C}^n, \|x\|_p=1} ...
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Norm of the linear bounded operator $T$ defined by $(Tf)(x) = \int_0^x g(t)f(t)dt$

Some time ago my teacher showed the solution of this exercise. Today I reviewed it, and I think he might be wrong at the last part, c.) Exercise: Let $a > 0$ and let $g \in C[0,a]$ be a ...
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Norm of an operator on space of real polynomials

Let $L:\mathbb{R}[X]\rightarrow\mathbb{R}[X]$ be an operator given by the following formula $L\left(\sum\limits_n a_nX^n\right)=\sum\limits_n a_{2n}X^{2n}$. We assume that on $\mathbb{R}[X]$ we have ...
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Euclidean norm gives length even in $>3$ dimensions?

In $1,2,3$ dimensions I can simply make triangles and see that Euclidean norm gives me the distance between two points (i.e. the length of the vector from one point to the other). In higher ...
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Does $\mathbf x\cdot \mathbf y = 0$ imply that $\lVert x+y\rVert_1 = \lVert x\rVert_1 + \lVert y\rVert_1$?

If x and y are orthogonal vectors and we define $\lVert x\rVert_1 =\sum^{n}_{j=1} |x_j|$, is it possible to express $\lVert x+y\rVert_1$ in terms of $\lVert x\rVert_1$ and $\lVert y\rVert_1$ ? So I ...
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Simple Euclidean Norm Inequality

I feel rather silly for having to ask this question in specific and am by no means looking for a flat out step by step answer. I understand the definition for the euclidean norm in an n-dimensional ...
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If $V$ is a finite dimensional with two norms then $\Vert v\Vert_1 \leq c\Vert v\Vert_2 $

Suppose $V$ is finite-dimensional and $\langle\cdot,\cdot\rangle_1$ and $\langle\cdot,\cdot\rangle_2$ are inner products on $V$ with corresponding norms $\Vert \cdot\Vert_1$ and $\Vert \cdot\Vert_2$. ...
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Orthogonal Projection - property of an orthogonal operator or something that needs to be proven?

I am currently working on showing that $\Vert Px \Vert_{2} \leq \Vert x \Vert_{2}$, where $x \in$ an inner product space $X$, and $P$ is the orthogonal projection operator. Also, I am supposing that ...
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Proving inequality for norm of linear transformation

Stumbled upon this one in a textbook: Let there be a linear transformation $T:V\rightarrow V$ over a finite inner product space $V$. It is known that $TT^* = 7T - 12I$. How can it be proved that ...
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Hahn-Banach separation theorem with a countable subset of functionals

For a separable Banach space $X$, the unit sphere of $X^*$ always contains a countable set $D$ such that $$ \left\Vert x \right\Vert = \sup_{f \in D} \left\vert f(x) \right\vert \qquad \mbox{ for ...
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Time derivative of the distance between 2 points moving over time

let $d_{ij}$ the distance between 2 points in space $p_i$ and $p_j$. These 2 points are moving over time so it is more correct to write them as $p_i(t)$ and $p_j(t)$. $p_i$ and $p_j$ are, at every ...
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Show $\|f\|_{C[0,1]} \leq C \|f\|_{H^1[0,1]}$

My question: If $f \in C^1[0,1]$, show that $\|f\|_C[0,1] \leq C\|f\|_{H^1[0,1]}$, where $C$ is a constant independent of $f$ and $\|f\|^2_{H^1[0,1]}:= \|f\|^2_{L^2[0,1]}+\|f'\|^2_{L^2[0,1]}$. ...
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Why is the norm ball a square in $\,\mathbb R^2\,$ under $\,l^\infty\,$ norm?

Suppose $\,x = \left(x_1, x_2\right)$, then $\,l^2\,$ norm ball is $\,\left\lbrace x\;\big\vert\;\, \sqrt{\left\lvert x_1 \right\rvert^2 + \left\lvert x_2 \right\rvert^2} \leq 1\right\rbrace$ Easily ...
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Matrix tensor indices

Suppose I have an orthonormal basis $$B = \left \{ u_{i} \right \}_{i=1}^{\infty}$$ Then for a matrix $K$, do I represent it as $$K = \sum_{j,k=0}^{\infty}k_{jk}\left ( u_{i}\bigotimes u_{j} ...
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Understanding convergence in normed spaces and the language used when talking about norms.

We have the following definition about convergence in a normed space: "Let $(x_n)_{n=1}^\infty$ be a sequence in a normed space $(X,\|\cdot\|)$. We say that $x_n\to x$ in $X$ if, $$d(x_n,x)\equiv ...
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Frobenius norm of product of matrix

The frobenius norm of matrix $F$ with dimension $m\times n$ is defined as $$||F||^2_F = \sum_{i=1}^m\sum_{j=1}^n |f_{i,j}|^2$$ If I have the multiplication of two matrices $$FG$$ where G is matrix ...
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(Operator) norm inequality for continuous functions

Let $f,g$ be two non-negative continuous functions on $[0,\infty)$ satisfying $f(t)g(t)=t,$ $\forall t\in[0,\infty)$. Let be $A$ be a bounded linear operator acting on a Hilbert space. Then I was ...
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Understanding Norms On a Vector Space (Part II)

This question is motivated by a previous question of mine. Let $\|\cdot \|$ be any norm(not necessarily the standard norm) on $\mathbf R^3$ and $S$ be the set of all the points with norm $1$. Let $p$ ...
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Understanding Norms on Vector Spaces

Let $\|\cdot\|$ be a norm (not necessarily the standard norm) on $\mathbf R^2$ and $S$ be the set of all the vectors $v$ such that $\|v\|=1$. For any point $p\in S$, let $\ell_p$ denote the line ...
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Proof: $|||AB|||^ \frac{1}{2} \leq |||A ^ \frac{1}{2}B ^ \frac{1}{2}||| $?

I am looking for a proof of the following: \begin{equation*} |||AB|||^ \frac{1}{2} \leq |||A ^ \frac{1}{2}B ^ \frac{1}{2}||| \end{equation*} Where A, B are positive, hermitian matrices, and $|||⋅|||$ ...
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Norm of orthonormal basis

I know that an orthonormal basis of a vector space, say V is a orthogonal basis in which each entry has unit length. My question is, then, if you have some orthonormal basis say $\{v_1,\ldots,v_8\}$ ...
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Rank as norm on matrix

Could we consider matrix rank $r$ a norm? Is other norm similar to rank $r$ possible to associate with a finite matrix? (We denote trivial valuation $|\alpha|=1,\quad\forall\alpha\in\Bbb F^*$ where ...
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For invertible $A$ show that $\lbrace y \in \mathbb{R}^n : \| x-y \|_A < r \rbrace= \lbrace x + A^{-1} y: y \in B_r(0) \rbrace$

I am struggling with the following Problem: Let $| \cdot|$ be the Euclidian Norm on $\mathbb{R}^n$. Let $A$ be an invertible $n \times n$ Matrix. Define $\|x\|_A = |Ax|$ for $x \in \mathbb{R}^n$ ...
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Interpreting norm definition

Book: Convex Optimization (Author: Stephen Boyd), Appendix A, Topic: A.1.2 Norm,distance, and unit ball Can anyone please help me in understanding the following definition of "norm" $$ \| x \| ...
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Derivative of a norm vs norm of a derivative

Consider a vector-valued function of the time, say $$v: \tau\in\mathbb{R}\to\mathbb{R}_N.$$ Suppose that for $\tau=t$, the function is equal to the zero vector, i.e. $$v(t)=0_N.$$ Denote as ...
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Counterexample for $\| (\Phi^\top \Xi \Phi)^\dagger \Phi^\top \Xi P \Phi \|_2 \leq 1$

All matrices are real. Let $P$ be any stochastic matrix (i.e. square, non-negative, rows summing to one). Let $\Xi$ be a diagonal matrix containing on its diagonal any left eigenvector of $P$ ...
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290 views

a question about symmetric positive definite matrix and norm

If B is $n\times n$ real symmetric positive definite matrix, then $(x,y)=x^TBy$ definites an inner product on $R^n$. How to prove that $||x||=(x^TBx)^{1/2}$ is a norm on $R^n$?
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Proving result on spectral radius

How do I prove that $$\rho(A)=\inf\limits_{\text{operator norms}}\|A\|,$$ $\rho$ being the spectral radius, $A$ being a complex $n\times n$ matrix and operator norms being induced from vector norms by ...
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Norm of symmetric positive semidefinite matrices

I have been researching a lot trying to find an answer to my question and didn't find any so I would appreciate it if anyone can help. If we have 2 symmetric, positive semi-definite matrices $A$ and ...
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Where does the definition of the $L_0$ norm come from?

Where does the definition of the $L_0$ norm come from? $$\|x\|_0=|S|$$ Where $S=\{x_k:x_k\neq 0\}$
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Is $||A||_F ||x||_2^2 \geq x^TAx$

Given a symmetric matrix $A$ and a vector $x$ Is $||A||_F ||x||_2^2 \geq x^TAx$? If yes, how to show this?
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Matrix Norm Inequalities and Linear Least Squares

I am working through one of my universities old QUALS and came across the following problem: Let $A,E\in \mathbb{C}^{m\times m}$. Suppose that $\sigma_\min>0$ is the smallest singular value of ...
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Spectral norm of a matrix obtained by setting some entries to zero

For example can we say, that if $A$ is original matrix and $A'$ obtained from $A$ by zeroing some elements then $\|A\|_2 \geq \|A'\|_2$?
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Inequality of Weighted norm

I have a question about the weighted norm inequality: The weighted norm of a vector $x\in R^{M\times N}$ is defined by: $\left \| X\right \|_{w,*} = \sum_{_{i}}\left |w_{i}\sigma _{i}\left ( X ...
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What is the usual norm of $\mathbb{H_0^1}$?

What is the usual norm of product of sobolev spaces $\mathbb{H_0^1}=H_0^1 \times H_0^1=W^{1,2}\times W^{1,2}$? In my work I need to prove that the norm endowed by the inner product $$ ...
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Derivative of a Vector with respect to its norm (special relativity)

I came across an equation (related to special relativity) that requires me to to take a derivative of a vector with respect to to it's own norm. In a bit more detail, what I mean is, let: $$\vec ...
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Comparable norms on the space of polynomials?

Are the norms: $$\|P\|_1=\int^1_0\|P(t) \| dt\mbox{ and }\|P\|_2=\sup_{0\le t\le1} |P(t)|.t$$ comparable on the vector space $X=\mathcal{P}[0,1]$, i.e. all polynomials on $[0,1]$? Here, i try to ...
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Norm of vector with respect to operator

Define $L$ is a linear operator maps from $E^n$ to $\mathbb{R}$, its norm is defined as $||L||_{op}=\sup\limits_{||x||=1}L(x)$, where $||\cdot||$ is any norm on $E^n$. How to show that ...
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Weak Lower Semicontinuity Generalized to any $L_{p}$ space

I am having difficulty with the following proof: Generalize the weak lower semi-continuity of$L^{p}$ norms to all $1\leq p < \infty$; i.e., show that if $u_{n}\to u$ weakly in $L^{p}$, then ...
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Any positive linear functional $\phi$ on $\ell^\infty$ is a bounded linear operator and has $\|\phi \| = \phi((1,1,…)) $

This is a small exercise that I just can't seem to figure out. When I see it I'll probably go 'ahhh!', but so far I haven't made any progress. I'd like to prove that any linear functional $\phi$ on ...
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how to prove that the limit of this sequence of functions is continuous?

I have a norm that works in function space and that is $‖∙‖_{sup}:C([0,1])→R$, $‖∙‖_{sup}:=sup${$|f(t)|$}. I need to show that the metric is complete. So I need to show that every Cauchy sequence of ...
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Why is the weak* topology not in general metrizable?

A Banach space is a topological group under addition. The dual is a topological group under the weak$^*$ topology. The weak$^*$ topology is weaker than the operator norm topology, so is it ...
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matrix norm derivative with respect a parameter

What is the result of the following expression $\frac{d}{dt}\left( \|A(t)-B(t) \|\right) $, where $\|\cdot \|$ can be for instance the Frobenius norm?
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Induced norm question.

I'd like to show that the induced 1-norm satisfies: $\|A\|_1=\max_{1 \le j \le n}\sum_{i=1}^n |a_{i,j}|$ I'd appreciate some guidance.
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Vector norm Inequality proof

Does anyone know how to start proving this inequality $$ \left\|\frac{x}{\|x\|} - \frac{y}{\|y\|}\right\| \leq \frac{4 \|x-y\|}{\|x\|+ \|y\|} $$ The norm is a random norm on a vector space $V$
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Operator attains norm

We have the following linear and well-defined mapping $T_{a}(b):=\sum_{j=1}^{\infty}a_{j}b_{j}$, with $\{a_{j}\}\in \ell^{1}$ and $\{b_{j}\} \in c_{0}$. Show that $\|T_{a}\|=\|a\|_{1}$. My work: ...
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Matrix Norm Bounds

A natural consideration of matrix norms is to compare them, and one of the many standard results on the induced 1, 2, and $\infty$-norms indicate that $$\frac{1}{\sqrt{n}}\|A\|_\infty\leq \|A\|_2\leq ...
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Proving that $\|A\|_{\infty}$ the largest row sum of absolute value of matrix $A$

I am studying matrix norms. I have read that $\|A\|_{\infty}$ is the largest row sum of absolute value and $\|A\|_{1}$ is the highest column sum of absolute values of the matrix $A$. However, I am ...
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Calculating the norm of $S\otimes T$ for bounded linear $S,T$.

Let $X,Y,W,Z$ be Banach Spaces. Let $X\otimes_{\pi}Y$ denote the tensor product endowed with the projective norm. If I have $S\in B(X,Z)$, $T\in B(Y,W)$, it is straightforward to show that $S\otimes ...