Improve performance of opnorm for (m x 1) and (1 x n) sparse matrices (#32372)

* Improve performance of opnorm for (m x 1) and (1 x n) sparse matrices

* Restrict values of p to 1, 2, Inf

* Use nzvalview instead of A.nzval

Author: goggle

stdlib/SparseArrays/src/linalg.jl

@@ -1028,9 +1028,16 @@ function opnorm(A::SparseMatrixCSC, p::Real=2)
     m, n = size(A)
     if m == 0 || n == 0 || isempty(A)
         return float(real(zero(eltype(A))))
-    elseif m == 1 || n == 1
-        # TODO: compute more efficiently using A.nzval directly
-        return opnorm(Array(A), p)
+    elseif m == 1
+        if p == 1
+            return norm(nzvalview(A), Inf)
+        elseif p == 2
+            return norm(nzvalview(A), 2)
+        elseif p == Inf
+            return norm(nzvalview(A), 1)
+        end
+    elseif n == 1 && p in (1, 2, Inf)
+        return norm(nzvalview(A), p)
     else
         Tnorm = typeof(float(real(zero(eltype(A)))))
         Tsum = promote_type(Float64,Tnorm)

stdlib/SparseArrays/test/sparse.jl

@@ -1803,6 +1803,20 @@ end
     resize!(foo.nzval, 5)
     setindex!(foo.nzval, NaN, 5)
     @test norm(foo) == 2.0
+
+    # Test (m x 1) sparse matrix
+    colM = sprandn(10, 1, 0.6)
+    @test opnorm(colM, 1) ≈ opnorm(Array(colM), 1)
+    @test opnorm(colM) ≈ opnorm(Array(colM))
+    @test opnorm(colM, Inf) ≈ opnorm(Array(colM), Inf)
+    @test_throws ArgumentError opnorm(colM, 3)
+
+    # Test (1 x n) sparse matrix
+    rowM = sprandn(1, 10, 0.6)
+    @test opnorm(rowM, 1) ≈ opnorm(Array(rowM), 1)
+    @test opnorm(rowM) ≈ opnorm(Array(rowM))
+    @test opnorm(rowM, Inf) ≈ opnorm(Array(rowM), Inf)
+    @test_throws ArgumentError opnorm(rowM, 3)
 end
 
 @testset "sparse matrix cond" begin