formatter, bump version (#195)

ZIB-IOL · Jun 24, 2021 · ec66a6c · ec66a6c
1 parent 75c74eb
commit ec66a6c
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Showing 8 changed files with 81 additions and 79 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "FrankWolfe"
 uuid = "f55ce6ea-fdc5-4628-88c5-0087fe54bd30"
 authors = ["ZIB-IOL"]
-version = "0.1.2"
+version = "0.1.3"
 
 [deps]
 Arpack = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"

diff --git a/docs/make.jl b/docs/make.jl
@@ -5,12 +5,13 @@ using LinearAlgebra
 makedocs(
     modules=[FrankWolfe],
     sitename="FrankWolfe.jl",
-    format=Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),
-    pages=["Home"=>"index.md","Examples"=>"examples2.md","References"=>"reference.md",
-            "Index"=>"indexlist.md"],
+    format=Documenter.HTML(prettyurls=get(ENV, "CI", nothing) == "true"),
+    pages=[
+        "Home" => "index.md",
+        "Examples" => "examples2.md",
+        "References" => "reference.md",
+        "Index" => "indexlist.md",
+    ],
 )
 
-deploydocs(
-    repo   = "github.com/ZIB-IOL/FrankWolfe.jl.git",
-    push_preview = true,
-)
+deploydocs(repo="github.com/ZIB-IOL/FrankWolfe.jl.git", push_preview=true)
diff --git a/examples/lazy_away_step_cg-sparsity.jl b/examples/lazy_away_step_cg-sparsity.jl
@@ -92,8 +92,8 @@ x0 = deepcopy(x00)
     emphasis=FrankWolfe.memory,
     verbose=true,
     trajectory=true,
-    callback=callback
-    );
+    callback=callback,
+);
 
 
 println("\n==> Lazy AFW.\n")
@@ -115,7 +115,7 @@ x0 = deepcopy(x00)
     lazy=true,
     K=1.5,
     trajectory=true,
-    callback=callback
+    callback=callback,
 );
 
 
@@ -136,7 +136,7 @@ x0 = deepcopy(x00)
     lazy=true,
     K=2.0,
     trajectory=true,
-    callback=callback
+    callback=callback,
 );
 
 
@@ -157,7 +157,7 @@ x0 = deepcopy(x00)
     K=4.0,
     lazy=true,
     trajectory=true,
-    callback=callback
+    callback=callback,
 );
 
 trajectoryAdaLoc10 = []
@@ -177,7 +177,7 @@ x0 = deepcopy(x00)
     K=10.0,
     verbose=true,
     trajectory=true,
-    callback=callback
+    callback=callback,
 );
 
 

diff --git a/examples/linear_regression.jl b/examples/linear_regression.jl
@@ -70,7 +70,8 @@ params = rand(6) .- 1 # start params in (-1,0)
       norm(FrankWolfe.compute_gradient(f_stoch_noisy, params))
 
 # test that error at true parameters is lower than at randomly initialized ones
-@test FrankWolfe.compute_value(f_stoch_noisy, params) > FrankWolfe.compute_value(f_stoch_noisy, params_perfect)
+@test FrankWolfe.compute_value(f_stoch_noisy, params) >
+      FrankWolfe.compute_value(f_stoch_noisy, params_perfect)
 
 # Vanilla Stochastic Gradient Descent with reshuffling
 for idx in 1:1000

diff --git a/src/afw.jl b/src/afw.jl
@@ -31,11 +31,11 @@ function away_frank_wolfe(
     renorm_interval=1000,
     callback=nothing,
     timeout=Inf,
-    print_callback=FrankWolfe.print_callback    
+    print_callback=FrankWolfe.print_callback,
 )
-    
-     # format string for output of the algorithm
-     format_string = "%6s %13s %14e %14e %14e %14e %14e %14i\n"
+
+    # format string for output of the algorithm
+    format_string = "%6s %13s %14e %14e %14e %14e %14e %14i\n"
 
     t = 0
     dual_gap = Inf
@@ -74,7 +74,7 @@ function away_frank_wolfe(
         end
         headers =
             ("Type", "Iteration", "Primal", "Dual", "Dual Gap", "Time", "It/sec", "#ActiveSet")
-        print_callback(headers,format_string,print_header=true)
+        print_callback(headers, format_string, print_header=true)
     end
 
     # likely not needed anymore as now the iterates are provided directly via the active set
@@ -114,7 +114,7 @@ function away_frank_wolfe(
         end
 
         #####################
-        
+
         # compute current iterate from active set
         x = compute_active_set_iterate(active_set)
         if isnothing(momentum)
@@ -202,7 +202,7 @@ function away_frank_wolfe(
                 t / tot_time,
                 length(active_set),
             )
-            print_callback(rep,format_string)
+            print_callback(rep, format_string)
             flush(stdout)
         end
         t += 1
@@ -230,7 +230,7 @@ function away_frank_wolfe(
             t / ((time_ns() - time_start) / 1.0e9),
             length(active_set),
         )
-        print_callback(rep,format_string)
+        print_callback(rep, format_string)
         flush(stdout)
     end
 
@@ -253,8 +253,8 @@ function away_frank_wolfe(
             t / ((time_ns() - time_start) / 1.0e9),
             length(active_set),
         )
-        print_callback(rep,format_string)
-        print_callback(nothing,format_string,print_footer=true)
+        print_callback(rep, format_string)
+        print_callback(nothing, format_string, print_footer=true)
         flush(stdout)
     end
 

diff --git a/src/blended_cg.jl b/src/blended_cg.jl
@@ -34,12 +34,12 @@ function blended_conditional_gradient(
     direction_storage=nothing,
     callback=nothing,
     timeout=Inf,
-    print_callback=FrankWolfe.print_callback,    
+    print_callback=FrankWolfe.print_callback,
     lmo_kwargs...,
 )
-    
-     # format string for output of the algorithm
-     format_string = "%6s %13s %14e %14e %14e %14e %14e %14i %14i\n"
+
+    # format string for output of the algorithm
+    format_string = "%6s %13s %14e %14e %14e %14e %14e %14i %14i\n"
 
     t = 0
     primal = Inf
@@ -99,7 +99,7 @@ function blended_conditional_gradient(
             "#ActiveSet",
             "#non-simplex",
         )
-        print_callback(headers,format_string,print_header=true)
+        print_callback(headers, format_string, print_header=true)
     end
     if !isa(x, Union{Array,SparseVector})
         x = convert(Array{float(eltype(x))}, x)
@@ -151,7 +151,7 @@ function blended_conditional_gradient(
             callback=callback,
             timeout=timeout,
             print_callback=print_callback,
-            format_string=format_string
+            format_string=format_string,
         )
         t += num_simplex_descent_steps
         #Take a FW step.
@@ -231,16 +231,16 @@ function blended_conditional_gradient(
                 t / tot_time,
                 length(active_set),
                 non_simplex_iter,
-            )    
-            print_callback(rep,format_string)
+            )
+            print_callback(rep, format_string)
             flush(stdout)
         end
 
         t = t + 1
         non_simplex_iter += 1
 
     end
-    
+
     ## post-processing and cleanup after loop
 
     # report last iteration
@@ -262,7 +262,7 @@ function blended_conditional_gradient(
             length(active_set),
             non_simplex_iter,
         )
-        print_callback(rep,format_string)
+        print_callback(rep, format_string)
         flush(stdout)
     end
 
@@ -290,8 +290,8 @@ function blended_conditional_gradient(
             length(active_set),
             non_simplex_iter,
         )
-        print_callback(rep,format_string)
-        print_callback(nothing,format_string,print_footer=true)
+        print_callback(rep, format_string)
+        print_callback(nothing, format_string, print_footer=true)
         flush(stdout)
     end
     return x, v, primal, dual_gap, traj_data
@@ -334,7 +334,7 @@ function minimize_over_convex_hull!(
     callback,
     timeout=Inf,
     print_callback=nothing,
-    format_string=nothing
+    format_string=nothing,
 )
     #No hessian is known, use simplex gradient descent.
     if hessian === nothing
@@ -357,7 +357,7 @@ function minimize_over_convex_hull!(
             callback=callback,
             timeout=timeout,
             print_callback=print_callback,
-            format_string=format_string
+            format_string=format_string,
         )
     else
         x = compute_active_set_iterate(active_set)
@@ -415,7 +415,7 @@ function minimize_over_convex_hull!(
                         callback=callback,
                         timeout=timeout,
                         print_callback=print_callback,
-                        format_string=format_string
+                        format_string=format_string,
                     )
                 @. active_set.weights = new_weights
             end
@@ -437,7 +437,7 @@ function minimize_over_convex_hull!(
                 callback=callback,
                 timeout=timeout,
                 print_callback=print_callback,
-                format_string=format_string    
+                format_string=format_string,
             )
             @. active_set.weights = new_weights
         end
@@ -583,7 +583,7 @@ function accelerated_simplex_gradient_descent_over_probability_simplex(
     callback,
     timeout=Inf,
     print_callback=nothing,
-    format_string=nothing
+    format_string=nothing,
 )
     number_of_steps = 0
     x = deepcopy(initial_point)
@@ -646,7 +646,7 @@ function accelerated_simplex_gradient_descent_over_probability_simplex(
                 length(initial_point),
                 non_simplex_iter,
             )
-            print_callback(rep,format_string)
+            print_callback(rep, format_string)
             flush(stdout)
         end
         if timeout < Inf
@@ -683,7 +683,7 @@ function simplex_gradient_descent_over_probability_simplex(
     callback,
     timeout=Inf,
     print_callback=nothing,
-    format_string=nothing
+    format_string=nothing,
 )
     number_of_steps = 0
     x = deepcopy(initial_point)
@@ -726,7 +726,7 @@ function simplex_gradient_descent_over_probability_simplex(
                 length(initial_point),
                 non_simplex_iter,
             )
-            print_callback(rep,format_string)
+            print_callback(rep, format_string)
             flush(stdout)
         end
         if timeout < Inf
@@ -815,7 +815,7 @@ function simplex_gradient_descent_over_convex_hull(
     callback,
     timeout=Inf,
     print_callback=nothing,
-    format_string=nothing
+    format_string=nothing,
 )
     number_of_steps = 0
     L_inner = nothing
@@ -961,7 +961,7 @@ function simplex_gradient_descent_over_convex_hull(
                 length(active_set),
                 non_simplex_iter,
             )
-            print_callback(rep,format_string)
+            print_callback(rep, format_string)
             flush(stdout)
         end
         if timeout < Inf