Fix benchmark code (#94)

- fix bug
- fix formatting

Author: lostella

benchmark/benchmarks.jl

@@ -13,18 +13,18 @@ function ProximalAlgorithms.value_and_gradient_closure(
     x,
 )
     res = f.A * x - f.b
-    norm(res)^2, () -> f.A' * res
+    norm(res)^2 / 2, () -> f.A' * res
 end
 
 struct SquaredDistance{Tb}
     b::Tb
 end
 
-(f::SquaredDistance)(x) = norm(x - f.b)^2
+(f::SquaredDistance)(x) = norm(x - f.b)^2 / 2
 
 function ProximalAlgorithms.value_and_gradient_closure(f::SquaredDistance, x)
     diff = x - f.b
-    norm(diff)^2, () -> diff
+    norm(diff)^2 / 2, () -> diff
 end
 
 for (benchmark_name, file_name) in [
@@ -45,88 +45,88 @@ for (benchmark_name, file_name) in [
         m, n = size(A)
 
         SUITE[k]["ForwardBackward"] =
-            @benchmarkable solver(x0 = x0, f = f, g = g) setup = begin
-                solver = ProximalAlgorithms.ForwardBackward(tol = 1e-6)
+            @benchmarkable solver(x0=x0, f=f, g=g) setup = begin
+                solver = ProximalAlgorithms.ForwardBackward(tol=1e-6)
                 x0 = zeros($T, size($A, 2))
                 f = LeastSquares($A, $b)
                 g = NormL1($lam)
             end
 
         SUITE[k]["FastForwardBackward"] =
-            @benchmarkable solver(x0 = x0, f = f, g = g) setup = begin
-                solver = ProximalAlgorithms.FastForwardBackward(tol = 1e-6)
+            @benchmarkable solver(x0=x0, f=f, g=g) setup = begin
+                solver = ProximalAlgorithms.FastForwardBackward(tol=1e-6)
                 x0 = zeros($T, size($A, 2))
                 f = LeastSquares($A, $b)
                 g = NormL1($lam)
             end
 
         SUITE[k]["ZeroFPR"] =
-            @benchmarkable solver(x0 = x0, f = f, A = $A, g = g) setup = begin
-                solver = ProximalAlgorithms.ZeroFPR(tol = 1e-6)
+            @benchmarkable solver(x0=x0, f=f, A=$A, g=g) setup = begin
+                solver = ProximalAlgorithms.ZeroFPR(tol=1e-6)
                 x0 = zeros($T, size($A, 2))
                 f = SquaredDistance($b)
                 g = NormL1($lam)
             end
 
         SUITE[k]["PANOC"] =
-            @benchmarkable solver(x0 = x0, f = f, A = $A, g = g) setup = begin
-                solver = ProximalAlgorithms.PANOC(tol = 1e-6)
+            @benchmarkable solver(x0=x0, f=f, A=$A, g=g) setup = begin
+                solver = ProximalAlgorithms.PANOC(tol=1e-6)
                 x0 = zeros($T, size($A, 2))
                 f = SquaredDistance($b)
                 g = NormL1($lam)
             end
 
         SUITE[k]["PANOCplus"] =
-            @benchmarkable solver(x0 = x0, f = f, A = $A, g = g) setup = begin
-                solver = ProximalAlgorithms.PANOCplus(tol = 1e-6)
+            @benchmarkable solver(x0=x0, f=f, A=$A, g=g) setup = begin
+                solver = ProximalAlgorithms.PANOCplus(tol=1e-6)
                 x0 = zeros($T, size($A, 2))
                 f = SquaredDistance($b)
                 g = NormL1($lam)
             end
 
         SUITE[k]["DouglasRachford"] =
-            @benchmarkable solver(x0 = x0, f = f, g = g, gamma = $R(1)) setup = begin
-                solver = ProximalAlgorithms.DouglasRachford(tol = 1e-6)
+            @benchmarkable solver(x0=x0, f=f, g=g, gamma=$R(1)) setup = begin
+                solver = ProximalAlgorithms.DouglasRachford(tol=1e-6)
                 x0 = zeros($T, size($A, 2))
                 f = LeastSquares($A, $b)
                 g = NormL1($lam)
             end
 
         SUITE[k]["DRLS"] =
-            @benchmarkable solver(x0 = x0, f = f, g = g, Lf = Lf) setup = begin
-                solver = ProximalAlgorithms.DRLS(tol = 1e-6)
+            @benchmarkable solver(x0=x0, f=f, g=g, Lf=Lf) setup = begin
+                solver = ProximalAlgorithms.DRLS(tol=1e-6)
                 x0 = zeros($T, size($A, 2))
                 f = LeastSquares($A, $b)
                 Lf = opnorm(($A)' * $A)
                 g = NormL1($lam)
             end
 
         SUITE[k]["AFBA-1"] =
-            @benchmarkable solver(x0 = x0, y0 = y0, f = f, g = g, beta_f = beta_f) setup =
+            @benchmarkable solver(x0=x0, y0=y0, f=f, g=g, beta_f=beta_f) setup =
                 begin
                     beta_f = opnorm($A)^2
                     solver =
-                        ProximalAlgorithms.AFBA(theta = $R(1), mu = $R(1), tol = $R(1e-6))
+                        ProximalAlgorithms.AFBA(theta=$R(1), mu=$R(1), tol=$R(1e-6))
                     x0 = zeros($T, size($A, 2))
                     y0 = zeros($T, size($A, 2))
                     f = LeastSquares($A, $b)
                     g = NormL1($lam)
                 end
 
         SUITE[k]["AFBA-2"] =
-            @benchmarkable solver(x0 = x0, y0 = y0, h = h, L = $A, g = g) setup = begin
+            @benchmarkable solver(x0=x0, y0=y0, h=h, L=$A, g=g) setup = begin
                 beta_f = opnorm($A)^2
                 solver =
-                    ProximalAlgorithms.AFBA(theta = $R(1), mu = $R(1), tol = $R(1e-6))
+                    ProximalAlgorithms.AFBA(theta=$R(1), mu=$R(1), tol=$R(1e-6))
                 x0 = zeros($T, size($A, 2))
                 y0 = zeros($T, size($A, 1))
                 h = Translate(SqrNormL2(), -$b)
                 g = NormL1($lam)
             end
 
         SUITE[k]["SFISTA"] =
-            @benchmarkable solver(x0 = x0, f = f, Lf = Lf, g = g) setup = begin
-                solver = ProximalAlgorithms.SFISTA(tol = $R(1e-3))
+            @benchmarkable solver(x0=x0, f=f, Lf=Lf, g=g) setup = begin
+                solver = ProximalAlgorithms.SFISTA(tol=$R(1e-3))
                 x0 = zeros($T, size($A, 2))
                 f = LeastSquares($A, $b)
                 g = NormL1($lam)