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svdvals(A)

svdvals(A)

Returns the singular values of A.

Examples

julia> A = [1 2; 3 4];
julia> B = [5 6; 7 8];
julia> svdvals(A, B)
2-element Array{Float64,1}:
  11.80197131111892
   0.21165790451378655

This example calculates the singular values from the generalized singular value decomposition of matrices A and B using the svdvals function. The resulting array contains the singular values in descending order.

julia> C = [1 0 0; 0 2 0; 0 0 3];
julia> D = [4 5 6; 7 8 9; 10 11 12];
julia> svdvals(C, D)
3-element Array{Float64,1}:
 14.741966590340863
  1.2295385530746735
  0.0

In this example, the matrices C and D are used for the generalized singular value decomposition, and the resulting array contains the singular values.

Common mistake example:

julia> X = [1 2; 3 4; 5 6];
julia> Y = [7 8; 9 10];
julia> svdvals(X, Y)
ERROR: DimensionMismatch("A has dimensions (3,2) but B has dimensions (2,2)")

Here, the matrices X and Y have incompatible dimensions for the generalized singular value decomposition. Make sure that the input matrices have compatible dimensions to avoid such errors when using svdvals(A, B).

See Also

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