Scilab Function norm - matrix norms
Calling Sequence
- [y]=norm(x [,flag])
Parameters
- x
: real or complex vector or matrix (full or sparse storage)
- flag
: string (type of norm) (default value =2)
Description
For matrices
norm(x)
: or norm(x,2) is the largest singular value of x (max(svd(x))).
norm(x,1)
: The l_1 norm x (the largest column sum : maxi(sum(abs(x),'r')) ).
norm(x,'inf'),norm(x,%inf)
: The infinity norm of x (the largest row sum : maxi(sum(abs(x),'c')) ).
norm(x,'fro')
: Frobenius norm i.e. sqrt(sum(diag(x'*x)))
For vectors
norm(v,p)
: l_p norm (sum(v(i)^p))^(1/p) .
norm(v)
: =norm(v,2) : l_2 norm
norm(v,'inf')
: max(abs(v(i))).
Examples
A=[1,2,3];
norm(A,1)
norm(A,'inf')
A=[1,2;3,4]
max(svd(A))-norm(A)
A=sparse([1 0 0 33 -1])
norm(A)
See Also