Define MulStyle(::ScaledMap) in terms of wrapped map (#179)

Author: dkarrasch

Project.toml

@@ -1,6 +1,6 @@
 name = "LinearMaps"
 uuid = "7a12625a-238d-50fd-b39a-03d52299707e"
-version = "3.6.1"
+version = "3.6.2"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

src/scaledmap.jl

@@ -24,6 +24,8 @@ LinearAlgebra.issymmetric(A::ScaledMap) = issymmetric(A.lmap)
 LinearAlgebra.ishermitian(A::ScaledMap) = isreal(A.λ) && ishermitian(A.lmap)
 LinearAlgebra.isposdef(A::ScaledMap) = isposdef(A.λ) && isposdef(A.lmap)
 
+MulStyle(A::ScaledMap) = MulStyle(A.lmap)
+
 Base.transpose(A::ScaledMap) = A.λ * transpose(A.lmap)
 Base.adjoint(A::ScaledMap) = conj(A.λ) * adjoint(A.lmap)
 

test/scaledmap.jl

@@ -17,6 +17,15 @@ using Test, LinearMaps, LinearAlgebra
     @test @inferred ishermitian(B) == ishermitian(A)
     @test @inferred isposdef(B) == isposdef(A)
     @test @inferred transpose(B) == α * transpose(A)
+    A! = LinearMap((y, x) -> cumsum!(y, x), N)
+    L = 0.5A! + 0.5A! + 1.0A!
+    @test LinearMaps.MulStyle(L) === LinearMaps.ThreeArg()
+    L*x; y = similar(x)
+    @test mul!(y, L, x) ≈ 2cumsum(x)
+    LM = 0.5LinearMap(AM) + 0.5LinearMap(AM) + 1.0LinearMap(AM)
+    @test LM*x ≈ 2cumsum(x)
+    @test LinearMaps.MulStyle(LM) === LinearMaps.FiveArg()
+    @test iszero(@allocated mul!(y, LM, x))
 
     @test B == A * α
     @test B * x == α * (A * x)

test/uniformscalingmap.jl

@@ -21,10 +21,9 @@ using Test, LinearMaps, LinearAlgebra
     @test @inferred isposdef(Id)
     @test Id * v == v
     @test (2 * M' + 3 * I) * v == 2 * A'v + 3v
-    @test (3 * I + 2 * M') * v == 2 * A'v + 3v
+    @test (3 * I + 2 * M') * v ≈ 3v + 2 * A'v rtol=2eps()
     @test (2 * M' - 3 * I) * v == 2 * A'v - 3v
-    @test (3 * I - 2 * M') * v == -2 * A'v + 3v
-    @test (3 * I - 2 * M') * v == -2 * A'v + 3v
+    @test (3 * I - 2 * M') * v ≈ 3v - 2 * A'v rtol=2eps()
     @test transpose(LinearMap(2 * M' + 3 * I)) * v ≈ transpose(2 * A' + 3 * I) * v
     @test LinearMap(2 * M' + 0I)' * v ≈ (2 * A')' * v
     for λ in (0, 1, rand()), α in (0, 1, rand()), β in (0, 1, rand()), sz in (m, (m,5))