improve log1pmx, add Float32 implimentation

src/basicfuns.jl

@@ -288,31 +288,18 @@ $(SIGNATURES)
 Return `log(1 + x) - x`.
 
 Use naive calculation or range reduction outside kernel range.  Accurate ~2ulps for all `x`.
-This will fall back to the naive calculation for argument types different from `Float64`.
+This will fall back to the naive calculation for argument types different from `Float32, Float64`.
 """
-function log1pmx(x::Float64)
-    if !(-0.7 < x < 0.9)
+log1pmx(x::Real) = log1p(x) - x # Naive fallback
+
+function log1pmx(x::T) where T <: Union{Float32, Float64}
+    if !(T(-0.425) < x < T(0.4)) # accurate within 2 ULPs when log2(abs(log1p(x))) > 1.5
         return log1p(x) - x
-    elseif x > 0.315
-        u = (x-0.5)/1.5
-        return _log1pmx_ker(u) - 9.45348918918356180e-2 - 0.5*u
-    elseif x > -0.227
-        return _log1pmx_ker(x)
-    elseif x > -0.4
-        u = (x+0.25)/0.75
-        return _log1pmx_ker(u) - 3.76820724517809274e-2 + 0.25*u
-    elseif x > -0.6
-        u = (x+0.5)*2.0
-        return _log1pmx_ker(u) - 1.93147180559945309e-1 + 0.5*u
     else
-        u = (x+0.625)/0.375
-        return _log1pmx_ker(u) - 3.55829253011726237e-1 + 0.625*u
+        return _log1pmx_ker(x)
     end
 end
 
-# Naive fallback
-log1pmx(x::Real) = log1p(x) - x
-
 """
 $(SIGNATURES)
 
@@ -345,21 +332,30 @@ function logmxp1(x::Real)
 end
 
 # The kernel of log1pmx
-# Accuracy within ~2ulps for -0.227 < x < 0.315
-function _log1pmx_ker(x::Float64)
-    r = x/(x+2.0)
+# Accuracy within ~2ulps -0.227 < x < 0.315 for Float64
+# Accuracy <2.18ulps -0.425 < x < 0.425 for Float32
+# parameters foudn via Remez.jl, specifically:
+# g(x) = evalpoly(x, big(2)./ntuple(i->2i+1, 50))
+# p = T.(Tuple(ratfn_minimax(g, (1e-3, (.425/(.425+2))^2), 8, 0)[1]))
+function _log1pmx_ker(x::T) where T <: Union{Float32, Float64}
+    r = x / (x+2)
     t = r*r
-    w = @horner(t,
-                6.66666666666666667e-1, # 2/3
-                4.00000000000000000e-1, # 2/5
-                2.85714285714285714e-1, # 2/7
-                2.22222222222222222e-1, # 2/9
-                1.81818181818181818e-1, # 2/11
-                1.53846153846153846e-1, # 2/13
-                1.33333333333333333e-1, # 2/15
-                1.17647058823529412e-1) # 2/17
-    hxsq = 0.5*x*x
-    r*(hxsq+w*t)-hxsq
+    if T == Float32
+        p = (0.6666658f0, 0.40008822f0, 0.2827692f0, 0.26246136f0)
+    else
+        p = (0.6666666666666669,
+             0.3999999999997768,
+             0.2857142857784595,
+             0.2222222142048249,
+             0.18181870670924566,
+             0.15382646727504887,
+             0.1337701340211177,
+             0.11201972567415432,
+             0.143418239946679)
+    end
+    w = evalpoly(t, p)
+    hxsq = x*x/2
+    muladd(r, muladd(w, t, hxsq), -hxsq)
 end
 
 

test/basicfuns.jl

@@ -216,7 +216,8 @@ end
     @test log1pmx(2f0) ≈ log(3f0) - 2f0
 
     for x in -0.5:0.1:10
-        @test log1pmx(Float32(x)) ≈ Float32(log1pmx(x))
+        @test log1pmx(Float32(x)) ≈ Float32(log1pmx(x)) atol=3*eps(Float32(x))
+        @test log1pmx(x) ≈ Float64(log1pmx(big(x))) atol=3*eps(x)
     end
 end