Improve quadprod with columnar-only implementation

First, this removes the option to do row-wise quadratic products since
they aren't being used within this package anyway. That allows removing
the "keyword" argument for choosing which direction to apply.

Second, the original implementation was optimized for the fast vector
outer product (that I got added to SparseArrays in JuliaLang/julia#24980
and made it into Julia v1.2), but when scaling up to multiple columns
the performance was disastrous because the dispatch of a transposed
view led to generic matrix multiplication which did the full
dense-dense style loops. By not using views, we get the desired
sparse matrix multiplication instead.

src/numerics.jl

@@ -2,6 +2,7 @@
     using Compat # Compat@v3.23 for sincospi()
 end
 using SparseArrays
+using SparseArrays: AbstractSparseMatrix
 
 # COV_EXCL_START
 
@@ -23,18 +24,27 @@ unchecked_sqrt(x::T) where {T <: Integer} = unchecked_sqrt(float(x))
 unchecked_sqrt(x) = Base.sqrt(x)
 
 """
-    quadprod(A, b, n, dir=:col)
+    quadprod(A::AbstractSparseMatrixCSC, b::AbstractVecOrMat, n::Integer)
 
 Computes the quadratic product ``ABA^\\top`` efficiently for the case where ``B`` is all zero
-except for the `n`th column or row vector `b`, for `dir = :col` or `dir = :row`,
-respectively.
+except for a small number of columns `b` starting at the `n`th.
 """
-@inline function quadprod(A, b, n, dir::Symbol=:col)
-    if dir == :col
-        return (A * sparse(b)) * view(A, :, n)'
-    elseif dir == :row
-        return view(A, :, n) * (A * sparse(b))'
+function quadprod(A::AbstractSparseMatrix, b::AbstractVecOrMat, n::Integer)
+    size(b, 1) == size(A, 2) || throw(DimensionMismatch())
+
+    # sparse * dense naturally returns dense, but we want to dispatch to
+    # a sparse-sparse matrix multiplication, so forceably sparsify.
+    # - Tests with a few example matrices A show that `sparse(A * b)` is faster than
+    #   `A * sparse(b)`.
+    w = sparse(A * b)
+    p = n + size(b, 2) - 1
+
+    if ndims(w) == 1
+        # vector outer product using column view into matrix is fast
+        C = w * transpose(view(A, :, n))
     else
-        error("Unrecognized direction `dir = $(repr(dir))`.")
+        # views are not fast for multiple columns; subset copies are faster
+        C = w * transpose(A[:, n:p])
     end
+    return C
 end