improve doc of svd(). fixes #2358

JuliaLang · Mar 4, 2013 · cc4ae19 · cc4ae19
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diff --git a/doc/manual/getting-started.rst b/doc/manual/getting-started.rst
@@ -134,6 +134,8 @@ differences that may trip up Julia users accustomed to MATLAB:
    operators, ``<``, ``>``, ``!=``, etc.
 -  The elements of a collection can be passed as arguments to a function
    using ``...``, as in ``xs=[1,2]; f(xs...)``.
+-  Julia's ``svd`` returns singular values as a vector instead of as a
+   full diagonal matrix.
 
 Noteworthy differences from R
 -----------------------------

diff --git a/doc/stdlib/base.rst b/doc/stdlib/base.rst
@@ -2288,11 +2288,11 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 
 .. function:: svd(A, [thin]) -> U, S, V
 
-   Compute the SVD of A, returning ``U``, ``S``, and ``V`` such that ``A = U*S*V'``. If ``thin`` is ``true``, an economy mode decomposition is returned.
+   Compute the SVD of A, returning ``U``, vector ``S``, and ``V`` such that ``A == U*diagm(S)*V'``. If ``thin`` is ``true``, an economy mode decomposition is returned.
 
 .. function:: svdt(A, [thin]) -> U, S, Vt
 
-   Compute the SVD of A, returning ``U``, ``S``, and ``Vt`` such that ``A = U*S*Vt``. If ``thin`` is ``true``, an economy mode decomposition is returned.
+   Compute the SVD of A, returning ``U``, vector ``S``, and ``Vt`` such that ``A = U*diagm(S)*Vt``. If ``thin`` is ``true``, an economy mode decomposition is returned.
 
 .. function:: svdvals(A)