Speed up A_ldiv_B! for QR factorizations

This partially fixes #7887, approximately doubling the performance. For matrices of size 10x3, now `A_ldiv_B!` is not slower than computing the factorization in the first place (although they're still in the same ballpark, unfortunately). It will get much faster yet with better array views (`sub` is now by far the major bottleneck).

The major benefit is from eliminating calls to `cat`, however I noticed that the type instability of `A[:Q]` was also a noteworthy problem. Hence the `getq` method. An alternative would be to introduce a new type
```
immutable FactorizationComponent{S} end
```
and use it this way:
```julia
julia> A = rand(10,3); F = qrfact(A);

julia> immutable FactorizationComponent{S} end

julia> import Base.LinAlg: QRCompactWY, QRCompactWYQ

julia> import Base: getindex

julia> getindex(A::QRCompactWY, ::Type{FactorizationComponent{:Q}}) = QRCompactWYQ(A.factors,A.T)
getindex (generic function with 171 methods)

julia> i = FactorizationComponent{:Q}
FactorizationComponent{:Q} (constructor with 1 method)

julia> Base.return_types(getindex, typeof((F, :Q)))
1-element Array{Any,1}:
 Union(QRCompactWYQ{Float64},Array{Float64,2})

julia> Base.return_types(getindex, typeof((F, i)))
1-element Array{Any,1}:
 QRCompactWYQ{Float64}
```

It was scaling the existing values by `smin` and then appending `cmin`. If you check the next couple of lines, you'll see it does the scaling.