Merge pull request #8474 from JuliaLang/anj/doc

Correct documentation for least squres \ and make documentation for elementwise operations more precise.
JuliaLang · Sep 25, 2014 · 5a2d5ea · 5a2d5ea
2 parents 94093ce + be5d788
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diff --git a/doc/manual/arrays.rst b/doc/manual/arrays.rst
@@ -330,8 +330,8 @@ operator should be used for elementwise operations.
 5.  Binary Boolean or bitwise — ``&``, ``|``, ``$``
 
 Some operators without dots operate elementwise anyway when one argument is a
-scalar. These operators are ``*``, ``/``, ``\``, and the bitwise
-operators.
+scalar. These operators are ``*``, ``+``, ``-``, and the bitwise operators. The
+operators ``/`` and ``\`` operate elementwise when the denominator is a scalar.
 
 Note that comparisons such as ``==`` operate on whole arrays, giving a single
 boolean answer. Use dot operators for elementwise comparisons.

diff --git a/doc/stdlib/linalg.rst b/doc/stdlib/linalg.rst
@@ -17,7 +17,7 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 .. function:: \\(A, B)
    :noindex:
 
-   Matrix division using a polyalgorithm. For input matrices ``A`` and ``B``, the result ``X`` is such that ``A*X == B`` when ``A`` is square.  The solver that is used depends upon the structure of ``A``.  A direct solver is used for upper- or lower triangular ``A``.  For Hermitian ``A`` (equivalent to symmetric ``A`` for non-complex ``A``) the ``BunchKaufman`` factorization is used.  Otherwise an LU factorization is used. For rectangular ``A`` the result is the minimum-norm least squares solution computed by reducing ``A`` to bidiagonal form and solving the bidiagonal least squares problem.  For sparse, square ``A`` the LU factorization (from UMFPACK) is used.
+   Matrix division using a polyalgorithm. For input matrices ``A`` and ``B``, the result ``X`` is such that ``A*X == B`` when ``A`` is square.  The solver that is used depends upon the structure of ``A``.  A direct solver is used for upper- or lower triangular ``A``.  For Hermitian ``A`` (equivalent to symmetric ``A`` for non-complex ``A``) the ``BunchKaufman`` factorization is used.  Otherwise an LU factorization is used. For rectangular ``A`` the result is the minimum-norm least squares solution computed by a pivoted QR factorization of ``A`` and a rank estimate of A based on the R factor. For sparse, square ``A`` the LU factorization (from UMFPACK) is used.
 
 .. function:: dot(x, y)
               ⋅(x,y)