Add benchmarks with ADNLPModels.jl

benchmarks/Project.toml

@@ -0,0 +1,7 @@
+[deps]
+ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
+BundleAdjustmentModels = "0bf3e898-7c79-4b38-9ca6-2d9cc6573905"
+MatrixMarket = "4d4711f2-db25-561a-b6b3-d35e7d4047d3"
+NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

benchmarks/benchmarks.jl

@@ -0,0 +1,74 @@
+using BundleAdjustmentModels
+using ADNLPModels
+using NLPModels
+using Printf
+
+debug = false
+check_jacobian = false
+df = problems_df()
+# df = df[ ( df.nequ .≤ 600000 ) .& ( df.nvar .≤ 100000 ), :]
+nproblems = size(df)[1]
+
+@printf("| %21s | %7s | %7s | %22s | %15s |\n", "problem", "nequ", "nvar", "backend", "coord_residual")
+for i = 1:nproblems
+  name_pb = df[i, :name]
+
+  # Smaller problem of the collection
+  debug && (i == 1) && (name_pb = "problem-49-7776-pre")
+  debug && (i ≠ 1) && continue
+
+  ## BundleAdjustementModels
+  nls = BundleAdjustmentModel(name_pb)
+  meta_nls = nls_meta(nls)
+  Fx = similar(nls.meta.x0, meta_nls.nequ)
+  rows = Vector{Int}(undef, meta_nls.nnzj)
+  cols = Vector{Int}(undef, meta_nls.nnzj)
+  vals = similar(nls.meta.x0, meta_nls.nnzj)
+  x = rand(nls.meta.nvar)  # nls.meta.x0
+
+  # warm-start
+  residual(nls, nls.meta.x0)
+  residual!(nls, nls.meta.x0, Fx)
+  jac_structure_residual!(nls, rows, cols)
+  jac_coord_residual!(nls, x, vals)
+
+  # benchmarks
+  start_timer = time()
+  jac_coord_residual!(nls, x, vals)
+  end_timer = time()
+  timer_coord_residual = end_timer - start_timer
+  @printf("| %21s | %7s | %7s | %22s | %6.5f seconds |\n", name_pb, nls.nls_meta.nequ, nls.meta.nvar, "BundleAdjustmentModels", timer_coord_residual)
+
+  ## ADNLPModels
+  function F!(Fx, x)
+    residual!(nls, x, Fx)
+  end
+  nls2 = ADNLSModel!(F!, nls.meta.x0, meta_nls.nequ, nls.meta.lvar, nls.meta.uvar, jacobian_residual_backend = ADNLPModels.SparseADJacobian,
+                                                                                   jacobian_backend = ADNLPModels.EmptyADbackend,
+                                                                                   hessian_backend = ADNLPModels.EmptyADbackend,
+                                                                                   hessian_residual_backend = ADNLPModels.EmptyADbackend)
+  meta_nls2 = nls_meta(nls2)
+  rows2 = Vector{Int}(undef, meta_nls2.nnzj)
+  cols2 = Vector{Int}(undef, meta_nls2.nnzj)
+  vals2 = similar(nls2.meta.x0, meta_nls2.nnzj)
+
+  # Warm-start
+  jac_structure_residual!(nls2, rows2, cols2)
+  jac_coord_residual!(nls2, x, vals2)
+
+  # benchmarks
+  start_timer = time()
+  jac_coord_residual!(nls2, x, vals2)
+  end_timer = time()
+  timer2_coord_residual = end_timer - start_timer
+  @printf("| %21s | %7s | %7s | %22s | %6.5f seconds |\n", name_pb, nls2.nls_meta.nequ, nls2.meta.nvar, "ADNLPModels", timer2_coord_residual)
+
+  if check_jacobian
+    println(meta_nls.nnzj)
+    println(meta_nls2.nnzj)
+    J_nls = sparse(rows, cols, vals)
+    J_nls2 = sparse(rows2, cols2, vals2)
+    norm(J_nls - J_nls2) |> println
+    norm(J_nls - J_nls2, Inf) |> println
+  end
+end

benchmarks/sparsity_patterns.jl

@@ -0,0 +1,23 @@
+using BundleAdjustmentModels
+using ADNLPModels
+using NLPModels
+using Printf
+using SparseArrays
+using MatrixMarket
+
+df = problems_df()
+num_pb = size(df, 1)
+for i = num_pb:-1:num_pb-9
+  name_pb = df[i, :name]
+  println("Problem $i / $(num_pb) -- $(name_pb)")
+
+  ## BundleAdjustementModels
+  nls = BundleAdjustmentModel(name_pb)
+  meta_nls = nls_meta(nls)
+  m, n = meta_nls.nequ, meta_nls.nvar 
+  rows, cols = jac_structure_residual(nls)
+  vals = ones(Bool, meta_nls.nnzj)
+  J = sparse(rows, cols, vals, m, n)
+  display(J)
+  mmwrite(name_pb * ".mtx", J)
+end

src/BundleAdjustmentNLSFunctions.jl

@@ -144,8 +144,7 @@ function projection!(
   f,
   r2::AbstractVector,
 )
-  # θ = norm(r)
-  θ = sqrt(dot(r, r))
+  θ = norm(r)
 
   # k .= r ./ θ
   k1 = r[1] / θ