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Vector and matrix norms
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5 X$ I3 D8 [( Y& V% un = norm(v)
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n = norm(v,p)
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/ ~ v; q# Q: F% _0 R- [3 l) Jn = norm(X)1 E9 W1 f9 R) m/ g: e/ p) E, Q0 T. o
1 N- N A4 k! f5 e/ s& \n = norm(X,p)
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n = norm(X,'fro')" d$ |- `; j' D
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Description
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* {/ o3 E( z/ ~" v' j# bn = norm(v)返回向量v的欧几里德范数。该范数也称为2范数,向量幅度或欧几里德长度。
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6 A& H3 ^8 w8 T; A: w0 k: S) Nn = norm(v,p)返回广义向量p范数。
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3 [2 I @) N; V& X1 hn = norm(X)返回矩阵X的2范数或最大奇异值,其近似为max(svd(X))。3 n3 n- r& S. u5 Q- x$ x1 a( r
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n = norm(X,p)返回矩阵X的p范数,其中p为1,2或Inf:
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! B( b P& h3 c% O- 如果p = 1,则n是矩阵的最大绝对列和。
- 如果p = 2,则n近似为max(svd(X))。 这相当于norm(X)。
- 如果p = Inf,那么n是矩阵的最大绝对行和。8 q/ S* D' e; {+ y2 Z+ \) D) Q8 Y
0 ^6 W) j) X/ n. S& pn = norm(X,'fro')返回矩阵X的Frobenius范数。+ W1 w' T$ V7 V3 f* l$ p
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有关范数的基础知识,见上篇文章:MATLAB必备的范数的基础知识$ u: d/ R. P% E* I6 H, D
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下面举例说明:
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Vector Magnitude(向量幅度)
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7 ~- x3 a' X! h: q4 K) r7 @* ]- %Create a vector and calculate the magnitude.
- v = [1 -2 3];
- n = norm(v)
- % n = 3.7417 V/ y' i/ T" i/ f
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1-Norm of Vector
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- clear
- close all
- % Calculate the 1-norm of a vector, which is the sum of the element magnitudes.
- X = [-2 3 -1];
- n = norm(X,1)
- % n = 6
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. t3 \. y9 w% aEuclidean Distance Between Two Points
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- clear
- close all
- % Calculate the distance between two points as the norm of the difference between the vector elements.
- %
- % Create two vectors representing the (x,y) coordinates for two points on the Euclidean plane.
- a = [0 3];
- b = [-2 1];
- % Use norm to calculate the distance between the points.
- d = norm(b-a)9 u/ h, Z2 z- X& Z8 d/ B! Q3 |3 v
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d =6 B6 G7 ^! l* V3 f& W' u& `" p" T6 y
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2.8284
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几何上,两点之间的距离:
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7 @( @7 R2 H8 j: S" H9 {( I! D% I2-Norm of Matrix! ^( q5 B0 Y+ ^+ h3 g# H
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- clc
- clear
- close all
- % Calculate the 2-norm of a matrix, which is the largest singular value.
- X = [2 0 1;-1 1 0;-3 3 0];
- n = norm(X)
- % n = 4.7234
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" F+ e; K1 R, y+ v7 w, }Frobenius Norm of Sparse Matrix( U l' u4 l; V, e! ^9 O
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- clear
- close all
- % 使用'fro'计算稀疏矩阵的Frobenius范数,该范数计算列向量的2范数S(:)。
- S = sparse(1:25,1:25,1);
- n = norm(S,'fro')
- % n = 5
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