Update README

Author: dkarrasch

README.md

@@ -70,11 +70,11 @@ The LinearMaps package provides the following functionality:
     *   `ishermitian [=T<:Real && issymmetric]`: whether the function represents the multiplication with a hermitian matrix. If `true`, this will automatically enable `A' * x` and `transpose(A) * x`.
     *   `isposdef [=false]`: whether the function represents the multiplication with a positive definite matrix.
 
-*   `Base.Array(A::LinearMap)`, `Base.Matrix(A::LinearMap)`, `Base.convert(Matrix, A::LinearMap)` and `Base.convert(Array, A::LinearMap)`
+*   `Array(A::LinearMap)`, `Matrix(A::LinearMap)`, `convert(Matrix, A::LinearMap)` and `convert(Array, A::LinearMap)`
 
-    Create a dense matrix representation of the `LinearMap` object, by multiplying it with the successive basis vectors. This is mostly for testing purposes or if you want to have the explicit matrix representation of a linear map for which you only have a function definition (e.g. to be able to use its `transpose` or `adjoint`). This way, one may conveniently make `A` act on the columns of a matrix `X`, instead of interpreting `A * X` as a composed linear map: `Matrix(A * X)`.
+    Create a dense matrix representation of the `LinearMap` object, by multiplying it with the successive basis vectors. This is mostly for testing purposes or if you want to have the explicit matrix representation of a linear map for which you only have a function definition (e.g. to be able to use its `transpose` or `adjoint`). This way, one may conveniently make `A` act on the columns of a matrix `X`, instead of interpreting `A * X` as a composed linear map: `Matrix(A * X)`. For generic code, that is supposed to handle both `A::AbstractMatrix` and `A::LinearMap`, it is recommended to use `convert(Matrix, A*X)`.
 
-*   `SparseArrays.sparse(::LinearMap)`
+*   `SparseArrays.sparse(A::LinearMap)` and `convert(SparseMatrixCSC, A::LinearMap)`
 
     Create a sparse matrix representation of the `LinearMap` object, by multiplying it with the successive basis vectors. This is mostly for testing purposes or if you want to have the explicit sparse matrix representation of a linear map for which you only have a function definition (e.g. to be able to use its `transpose` or `adjoint`).