length in a vector space
In mathematics, a norm is a function from a real or complex vector space to the non-negative real numbers that behaves in certain ways like the distance from the origin: it commutes โ€ฆ Wikipedia
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Wikipedia
en.wikipedia.org โ€บ wiki โ€บ Norm_(mathematics)
Norm (mathematics) - Wikipedia
March 5, 2026 - The concept of unit circle (the set of all vectors of norm 1) is different in different norms: for the 1-norm, the unit circle is a square oriented as a diamond; for the 2-norm (Euclidean norm), it is the well-known unit circle; while for the infinity norm, it is an axis-aligned square.
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ResearchGate
researchgate.net โ€บ figure โ€บ Graph-of-the-2-norm-of-the-simulated-measurements-in-each-frame_fig4_323655514
Graph of the 2 norm of the simulated measurements in each frame. | Download Scientific Diagram
Download scientific diagram | Graph of the 2 norm of the simulated measurements in each frame. from publication: Dynamic Spike Super-resolution and Applications to Ultrafast Ultrasound Imaging | We consider the dynamical super-resolution problem ...
Discussions

normed spaces - Intuitive explanation of $L^2$-norm - Mathematics Stack Exchange
We know that the Euclidean norm measure the length of a vector in the Euclidean space, but what does the $L^2$-norm? Is there anyone could give me an intuitive (even a graph, if possible) explanation of that norm? More on math.stackexchange.com
๐ŸŒ math.stackexchange.com
May 31, 2016
The l2 norm for 2 variables resembles a circle right? What would the l1 norm and l3 norm look like?
I don't know what you mean by it's just a length. These images plot the lines of constant norm. For example, the points (0,1), (1/2,1/2), and (-1,0) all have an L1 norm of 1. Here's a plot comparing L1, L2, L3, and L-infinity: https://www.desmos.com/calculator/7hbjvyb02v More on reddit.com
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August 28, 2023
functional analysis - What does the graph of a norm look like - Mathematics Stack Exchange
I talking about functions of the form ||X||^p, when p>1 for different values of p. I know these are all convex functions, but I don't know how to graph them. More on math.stackexchange.com
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May 17, 2016
real analysis - Proving the graph norm is indeed a norm - Mathematics Stack Exchange
How to show the graph norm above is indeed a norm on $D(T)?$ ... Proof of 1: As $\|\cdot\|_X$ is a norm, we have $\|u\|_T^2=\|u\|_X^2+\|Tu\|_X^2\geq \|u\|_X^2>0$ if $u\neq 0$ and thus 1 is valid. More on math.stackexchange.com
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ScienceDirect
sciencedirect.com โ€บ science โ€บ article โ€บ pii โ€บ S0095895699919068
Norm-Graphs: Variations and Applications - ScienceDirect
May 25, 2002 - We describe several variants of the norm-graphs introduced by Kollรกr, Rรณnyai, and Szabรณ and study some of their extremal properties. Using these variants we construct, for infinitely many values of n, a graph on n vertices with more than ... 2n5/3 edges, containing no copy of K3, 3, thus slightly improving an old construction of Brown.
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Medium
medium.com โ€บ @amehsunday178 โ€บ a-comprehensive-and-intuitive-visualization-of-vector-lp-norms-in-3d-space-618ccb6ba8f6
A Comprehensive and Intuitive Visualization of Vector Lp Norms in 3D Space . | by Ameh Emmanuel Sunday | Medium
November 4, 2025 - The L1 norm (size) of a vector also known as the Taxicab or Manhattan norm, is the sum of the absolute values of the components of the vector. Norms are usually denoted with the double vertical bar symbol ||.|| which is known more formally as norm brackets. Given a vector vโ‚ in 2D dimensions (xโ‚,xโ‚‚) , the Lโ‚ norm is shown in the image below.
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arXiv
arxiv.org โ€บ abs โ€บ 1909.10987
[1909.10987] Two remarks on graph norms
September 24, 2019 - Abstract:For a graph $H$, its homomorphism density in graphs naturally extends to the space of two-variable symmetric functions $W$ in $L^p$, $p\geq e(H)$, denoted by $t(H,W)$. One may then define corresponding functionals $\|W\|_{H}:=|t(H,W)|^{1/e(H)}$ and $\|W\|_{r(H)}:=t(H,|W|)^{1/e(H)}$ and say that $H$ is (semi-)norming if $\|.\|_{H}$ is a (semi-)norm and that $H$ is weakly norming if $\|.\|_{r(H)}$ is a norm.
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Wolfram MathWorld
mathworld.wolfram.com โ€บ L2-Norm.html
L^2-Norm -- from Wolfram MathWorld
July 26, 2003 - The l^2-norm (also written "l^2-norm") |x| is a vector norm defined for a complex vector x=[x_1; x_2; |; x_n] (1) by |x|=sqrt(sum_(k=1)^n|x_k|^2), (2) where |x_k| on the right denotes the complex modulus.
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CodingNomads
codingnomads.com โ€บ what-is-l2-norm
What is L2 Norm?
Here, you see the generalized form of the Lp-norm. When P=2, you get the Euclidean norm or L2-norm; when P=1, you get the Manhattan norm or L1-norm.
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Top answer
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OK let's see if this helps you. Suppose you have two functions $f,g:[a,b]\to \mathbb{R}$. If someone asks you what is distance between $f(x)$ and $g(x)$ it is easy you would say $|f(x)-g(x)|$. But if I ask what is the distance between $f$ and $g$, this question is kind of absurd. But I can ask what is the distance between $f$ and $g$ on average? Then it is $$ \dfrac{1}{b-a}\int_a^b |f(x)-g(x)|dx=\dfrac{||f-g||_1}{b-a} $$ which gives the $L^1$-norm. But this is just one of the many different ways you can do the averaging: Another way would be related to the integral $$ \left[\int_a^b|f(x)-g(x)|^p dx\right]^{1/p}:=||f-g||_{p} $$ which is the $L^p$-norm in general.

Let us investigate the norm of $f(x)=x^n$ in $[0,1]$ for different $L_p$ norms. I suggest you draw the graphs of $x^{p}$ for a few $p$ to see how higher $p$ makes $x^{p}$ flatter near the origin and how the integral therefore favors the vicinity of $x=1$ more and more as $p$ becomes bigger. $$ ||x||_p=\left[\int_0^1 x^{p}dx\right]^{1/p}=\frac{1}{(p+1)^{1/p}} $$ The $L^p$ norm is smaller than $L^m$ norm if $m>p$ because the behavior near more points is downplayed in $m$ in comparison to $p$. So depending on what you want to capture in your averaging and how you want to define `the distance' between functions, you utilize different $L^p$ norms.

This also motivates why the $L^\infty$ norm is nothing but the essential supremum of $f$; i.e. you filter everything out other than the highest values of $f(x)$ as you let $p\to \infty$.

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There are several good answers here, one accepted. Nevertheless I'm surprised not to see the $L^2$ norm described as the infinite dimensional analogue of Euclidean distance.

In the plane, the length of the vector $(x,y)$ - that is, the distance between $(x,y)$ and the origin - is $\sqrt{x^2 + y^2}$. In $n$-space it's the square root of the sum of the squares of the components.

Now think of a function as a vector with infinitely many components (its value at each point in the domain) and replace summation by integration to get the $L^2$ norm of a function.

Finally, tack on the end of last sentence of @levap 's answer:

... the $L^2$ norm has the advantage that it comes from an inner product and so all the techniques from inner product spaces (orthogonal projections, etc) can be applied when we use the $L^2$ norm.

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Reddit
reddit.com โ€บ r/askmath โ€บ the l2 norm for 2 variables resembles a circle right? what would the l1 norm and l3 norm look like?
r/askmath on Reddit: The l2 norm for 2 variables resembles a circle right? What would the l1 norm and l3 norm look like?
August 28, 2023 - I don't know what you mean by it's just a length. These images plot the lines of constant norm. For example, the points (0,1), (1/2,1/2), and (-1,0) all have an L1 norm of 1.
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ResearchGate
researchgate.net โ€บ figure โ€บ Plot-of-the-L2-norm-as-a-function-of-k-such-that-the-estimated-f-x-is-calculated_fig1_326476887
Plot of the L2 norm as a function of k โ€ฒ such that the estimated f (x)... | Download Scientific Diagram
Download scientific diagram | Plot of the L2 norm as a function of k โ€ฒ such that the estimated f (x) is calculated using ฮฒ k for k = k min + k โ€ฒ. The points encircled in red are the 3 k โ€ฒ s chosen for Figure 2. from publication: Model ...
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Medium
montjoile.medium.com โ€บ l0-norm-l1-norm-l2-norm-l-infinity-norm-7a7d18a4f40c
L0 Norm, L1 Norm, L2 Norm & L-Infinity Norm | by Sara Iris Garcia | Medium
December 22, 2020 - And lastly, if the L0 norm is 2, it means that both username and password are incorrect. Also known as Manhattan Distance or Taxicab norm. L1 Norm is the sum of the magnitudes of the vectors in a space. It is the most natural way of measure distance between vectors, that is the sum of absolute difference of the components of the vectors. In this norm, all the components of the vector are weighted equally. ... As you can see in the graphic...
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Stack Exchange
math.stackexchange.com โ€บ questions โ€บ 1601715 โ€บ proving-the-graph-norm-is-indeed-a-norm
real analysis - Proving the graph norm is indeed a norm - Mathematics Stack Exchange
Proof of 2: As $T$ is linear and $\|\cdot\|_X$ is a norm, we have $$\|a u\|_T^2=\|a u\|_X^2+\|T(a u)\|_X^2=|a|^2\|u\|_X^2+|a|^2\|Tu\|_X^2=|a|^2\|u\|_T^2$$ for any scalar $a$ and thus 2 is valid.
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Zeroentropy
zeroentropy.dev โ€บ articles โ€บ 2-norm-vector
2-Norm Vector โ€” ZeroEntropy Blog
July 20, 2025 - A valid norm must satisfy the following ... โ€–ฮฑxโ€–โ‚‚ = |ฮฑ| ยท โ€–xโ€–โ‚‚. ... In 2D, the 2โ€‘norm gives the straight-line distance from the origin to a point (xโ‚, xโ‚‚)....
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ScienceDirect
sciencedirect.com โ€บ science โ€บ article โ€บ abs โ€บ pii โ€บ S0031320325005503
Efficient โ„“2,1-norm graph for robust semi-supervised classification - ScienceDirect
June 6, 2025 - This paper aims to develop a novel representation-based affinity graph construction method to reveal the true intrinsic subspace structures of data. First, we propose to construct an efficient and robust ... 1-norm minimization regularization and the adaptive distance penalty over the representation coefficient matrix. We theoretically prove that the ... 1-norm converges towards its nuclear norm and can serve as the convex relaxation of the nuclear norm. Moreover, as a mixed matrix norm of the ... 2-norm.
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Medium
medium.com โ€บ data-science โ€บ visualizing-regularization-and-the-l1-and-l2-norms-d962aa769932
Visualizing regularization and the L1 and L2 norms | by Chiara Campagnola | TDS Archive | Medium
March 29, 2023 - On the left we have a plot of the L1 and L2 norm for a given weight w. On the right, we have the corresponding graph for the slope of the norms. As we can see, both L1 and L2 increase for increasing asbolute values of w.
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ScienceDirect
sciencedirect.com โ€บ topics โ€บ mathematics โ€บ vector-norm
Vector Norm - an overview | ScienceDirect Topics
The vector 2-norm enjoys yet another property: it is orthogonally invariant. This means that for any n ร— n orthogonal matrix P ... In other words, multiplication of a vector, x, by an orthogonal matrix will likely rotate x, but the resulting vector Px has the same length. This is one of the reasons that orthogonal matrices are useful in computer graphics.
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Springer
link.springer.com โ€บ home โ€บ discrete & computational geometry โ€บ article
Two Remarks on Graph Norms | Discrete & Computational Geometry | Springer Nature Link
February 16, 2021 - This answers a question of Hatami, who proved that \(({\mathscr {W}},\Vert \cdot \Vert _{H})\) is uniformly smooth and uniformly convex whenever H is semi-norming and asked for a counterpart of his theorem for weakly norming graphs. Theorem 1.1 not only answers a natural question arising from a functional-analytic perspective, but is also meaningful in the theory of quasirandomness. In [4], Hatamiโ€™s theorem about uniform convexity and smoothness (see Theorem 2.2 for a precise statement) is the key ingredient in proving that every norming graph has the โ€˜step forcing propertyโ€™. By inspecting the proof in [4], one may see that the same conclusion for weakly norming graphs H (except forests) could also be obtained if \(\Vert \,{\cdot }\,\Vert _{r(H)}\) defined a uniformly convex space.
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Medium
medium.com โ€บ @bpchiv โ€บ visualizing-the-circles-of-p-norms-ab99411404a9
Visualizing the circles of p-norms | by Brian Chivers | Medium
October 25, 2018 - The L-โˆž norm is equivalent to the maximum absolute dimension in the distance between two points. I wonโ€™t provide the full proof here, but as all differences are multiplied by themselves to the infinite power, the largest difference will take over in the limit.
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MathWorks
mathworks.com โ€บ matlab โ€บ mathematics โ€บ linear algebra
norm - Vector and matrix norms - MATLAB
If p = 1, then the resulting 1-norm is the sum of the absolute values of the vector elements. If p = 2, then the resulting 2-norm gives the vector magnitude or Euclidean length of the vector.