From 3e3a4cebc659eaf5fb3cde1f4727bc5c25b96b3f Mon Sep 17 00:00:00 2001
From: Stephan Hilb <stephan@ecshi.net>
Date: Fri, 24 Jul 2020 12:13:41 +0200
Subject: [PATCH] migrate to new algorithm interface
---
Manifest.toml | 630 +++++------------------------------------------
Project.toml | 2 +-
src/DualTVDD.jl | 147 +++++++----
src/chambolle.jl | 69 +++---
src/common.jl | 72 ++----
src/dualtvdd.jl | 44 +++-
src/problems.jl | 16 --
src/projgrad.jl | 15 +-
test/runtests.jl | 15 +-
9 files changed, 261 insertions(+), 749 deletions(-)
diff --git a/Manifest.toml b/Manifest.toml
index 33160c4..09c2c24 100644
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -1,53 +1,11 @@
# This file is machine-generated - editing it directly is not advised
-[[AbstractFFTs]]
-deps = ["LinearAlgebra"]
-git-tree-sha1 = "051c95d6836228d120f5f4b984dd5aba1624f716"
-uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
-version = "0.5.0"
-
-[[AbstractNumbers]]
-deps = ["SpecialFunctions"]
-git-tree-sha1 = "924732f1a32f0e4b1e748b469d4604125bdf1d00"
-uuid = "85c772de-338a-5e7f-b815-41e76c26ac1f"
-version = "0.2.1"
-
-[[AbstractPlotting]]
-deps = ["AbstractNumbers", "ColorBrewer", "ColorSchemes", "ColorTypes", "Colors", "Contour", "DocStringExtensions", "FFMPEG", "FileIO", "FixedPointNumbers", "FreeType", "FreeTypeAbstraction", "GeometryBasics", "ImageMagick", "IntervalSets", "LinearAlgebra", "Markdown", "Observables", "Packing", "PlotUtils", "Printf", "Random", "Serialization", "Showoff", "SignedDistanceFields", "SparseArrays", "StaticArrays", "Statistics", "StructArrays", "UnicodeFun"]
-git-tree-sha1 = "cd373780f00edbd6429ceacb2572b55160c01886"
-uuid = "537997a7-5e4e-5d89-9595-2241ea00577e"
-version = "0.10.11"
-
[[Adapt]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "c88cfc7f9c1f9f8633cddf0b56e86302b70f64c5"
uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
version = "1.0.1"
-[[Arpack]]
-deps = ["Arpack_jll", "Libdl", "LinearAlgebra"]
-git-tree-sha1 = "2ff92b71ba1747c5fdd541f8fc87736d82f40ec9"
-uuid = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
-version = "0.4.0"
-
-[[Arpack_jll]]
-deps = ["Libdl", "OpenBLAS_jll", "Pkg"]
-git-tree-sha1 = "e214a9b9bd1b4e1b4f15b22c0994862b66af7ff7"
-uuid = "68821587-b530-5797-8361-c406ea357684"
-version = "3.5.0+3"
-
-[[ArrayInterface]]
-deps = ["LinearAlgebra", "Requires", "SparseArrays"]
-git-tree-sha1 = "649c08a5a3a513f4662673d3777fe6ccb4df9f5d"
-uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
-version = "2.8.7"
-
-[[AxisAlgorithms]]
-deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"]
-git-tree-sha1 = "a4d07a1c313392a77042855df46c5f534076fab9"
-uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950"
-version = "1.0.0"
-
[[Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
@@ -57,17 +15,6 @@ git-tree-sha1 = "3663bfffede2ef41358b6fc2e1d8a6d50b3c3904"
uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
version = "1.0.6+2"
-[[CEnum]]
-git-tree-sha1 = "62847acab40e6855a9b5905ccb99c2b5cf6b3ebb"
-uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
-version = "0.2.0"
-
-[[ColorBrewer]]
-deps = ["Colors", "JSON", "Test"]
-git-tree-sha1 = "61c5334f33d91e570e1d0c3eb5465835242582c4"
-uuid = "a2cac450-b92f-5266-8821-25eda20663c8"
-version = "0.4.0"
-
[[ColorSchemes]]
deps = ["ColorTypes", "Colors", "FixedPointNumbers", "Random", "StaticArrays"]
git-tree-sha1 = "7a15e3690529fd1042f0ab954dff7445b1efc8a5"
@@ -80,36 +27,12 @@ git-tree-sha1 = "c73d9cfc2a9d8433dc77f5bff4bddf46b1d78c20"
uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
version = "0.10.3"
-[[ColorVectorSpace]]
-deps = ["ColorTypes", "Colors", "FixedPointNumbers", "LinearAlgebra", "SpecialFunctions", "Statistics", "StatsBase"]
-git-tree-sha1 = "bd0c0c81a39923bc03f9c3b61d89ad816e741002"
-uuid = "c3611d14-8923-5661-9e6a-0046d554d3a4"
-version = "0.8.5"
-
[[Colors]]
deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Reexport"]
git-tree-sha1 = "db9c215e2d92379d341e40dd1d5be893af2bfeb2"
uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
version = "0.12.1"
-[[CommonSubexpressions]]
-deps = ["Test"]
-git-tree-sha1 = "efdaf19ab11c7889334ca247ff4c9f7c322817b0"
-uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
-version = "0.2.0"
-
-[[Compat]]
-deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "SHA", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"]
-git-tree-sha1 = "48c20c43e157c6eab6cf88326504ec042b05e456"
-uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
-version = "3.10.0"
-
-[[CompilerSupportLibraries_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "7c4f882c41faa72118841185afc58a2eb00ef612"
-uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "0.3.3+0"
-
[[Contour]]
deps = ["StaticArrays"]
git-tree-sha1 = "0b17db36e7e03f8437e0d1f55aea3e4a60c74353"
@@ -140,45 +63,10 @@ uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
deps = ["Mmap"]
uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab"
-[[DiffResults]]
-deps = ["StaticArrays"]
-git-tree-sha1 = "da24935df8e0c6cf28de340b958f6aac88eaa0cc"
-uuid = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
-version = "1.0.2"
-
-[[DiffRules]]
-deps = ["NaNMath", "Random", "SpecialFunctions"]
-git-tree-sha1 = "eb0c34204c8410888844ada5359ac8b96292cfd1"
-uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
-version = "1.0.1"
-
-[[Distances]]
-deps = ["LinearAlgebra", "Statistics"]
-git-tree-sha1 = "23717536c81b63e250f682b0e0933769eecd1411"
-uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-version = "0.8.2"
-
[[Distributed]]
deps = ["Random", "Serialization", "Sockets"]
uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
-[[Distributions]]
-deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "c4ed10355637fcb0725dc6a27060f74df24f13cd"
-uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.23.2"
-
-[[DocStringExtensions]]
-deps = ["LibGit2", "Markdown", "Pkg", "Test"]
-git-tree-sha1 = "88bb0edb352b16608036faadcc071adda068582a"
-uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
-version = "0.8.1"
-
-[[EllipsisNotation]]
-git-tree-sha1 = "65dad386e877850e6fce4fc77f60fe75a468ce9d"
-uuid = "da5c29d0-fa7d-589e-88eb-ea29b0a81949"
-version = "0.4.0"
-
[[FFMPEG]]
deps = ["FFMPEG_jll"]
git-tree-sha1 = "c82bef6fc01e30d500f588cd01d29bdd44f1924e"
@@ -191,94 +79,28 @@ git-tree-sha1 = "0fa07f43e5609ea54848b82b4bb330b250e9645b"
uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5"
version = "4.1.0+3"
-[[FFTW]]
-deps = ["AbstractFFTs", "FFTW_jll", "IntelOpenMP_jll", "Libdl", "LinearAlgebra", "MKL_jll", "Reexport"]
-git-tree-sha1 = "b6a74f6dfd9e9d16b765397dc90df03e5a00532e"
-uuid = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
-version = "1.2.1"
-
-[[FFTW_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "6c975cd606128d45d1df432fb812d6eb10fee00b"
-uuid = "f5851436-0d7a-5f13-b9de-f02708fd171a"
-version = "3.3.9+5"
-
-[[FileIO]]
-deps = ["Pkg"]
-git-tree-sha1 = "202335fd24c2776493e198d6c66a6d910400a895"
-uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
-version = "1.3.0"
-
-[[FillArrays]]
-deps = ["LinearAlgebra", "Random", "SparseArrays"]
-git-tree-sha1 = "44f561e293987ffc84272cd3d2b14b0b93123d63"
-uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "0.8.10"
-
-[[FiniteDiff]]
-deps = ["ArrayInterface", "LinearAlgebra", "Requires", "SparseArrays", "StaticArrays"]
-git-tree-sha1 = "fec7c2cb45c27071ef487fa7cae4fcac7509aa10"
-uuid = "6a86dc24-6348-571c-b903-95158fe2bd41"
-version = "2.3.2"
-
[[FixedPointNumbers]]
git-tree-sha1 = "3ba9ea634d4c8b289d590403b4a06f8e227a6238"
uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
version = "0.8.0"
-[[ForwardDiff]]
-deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "NaNMath", "Random", "SpecialFunctions", "StaticArrays"]
-git-tree-sha1 = "869540e4367122fbffaace383a5bdc34d6e5e5ac"
-uuid = "f6369f11-7733-5829-9624-2563aa707210"
-version = "0.10.10"
-
-[[FreeType]]
-deps = ["CEnum", "FreeType2_jll"]
-git-tree-sha1 = "e2986a14a511f9d289b69bbdf09691fea5203976"
-uuid = "b38be410-82b0-50bf-ab77-7b57e271db43"
-version = "3.0.0"
-
[[FreeType2_jll]]
deps = ["Bzip2_jll", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "7d900f32a3788d4eacac2bfa3bf5c770179c8afd"
uuid = "d7e528f0-a631-5988-bf34-fe36492bcfd7"
version = "2.10.1+2"
-[[FreeTypeAbstraction]]
-deps = ["ColorVectorSpace", "Colors", "FreeType", "GeometryBasics", "StaticArrays"]
-git-tree-sha1 = "2bd5787060aa9883ec258523b728a2adf5ca9cba"
-uuid = "663a7486-cb36-511b-a19d-713bb74d65c9"
-version = "0.8.0"
-
[[FriBidi_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "2f56bee16bd0151de7b6a1eeea2ced190a2ad8d4"
uuid = "559328eb-81f9-559d-9380-de523a88c83c"
version = "1.0.5+3"
-[[GLFW]]
-deps = ["GLFW_jll"]
-git-tree-sha1 = "7c47cfbfe9e1ff1cf658ff3e739eb1b56ee95cbe"
-uuid = "f7f18e0c-5ee9-5ccd-a5bf-e8befd85ed98"
-version = "3.2.2"
-
-[[GLFW_jll]]
-deps = ["Libdl", "Libglvnd_jll", "Pkg", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"]
-git-tree-sha1 = "3b04f1dd73f0efa81298a76c9515f306df06f613"
-uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89"
-version = "3.3.2+0"
-
-[[GLM]]
-deps = ["Distributions", "LinearAlgebra", "Printf", "Random", "Reexport", "SparseArrays", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns", "StatsModels"]
-git-tree-sha1 = "db0ace36f9dbe7b6a7a08434c5921377e9df2c72"
-uuid = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
-version = "1.3.9"
-
-[[GLMakie]]
-deps = ["AbstractPlotting", "ColorTypes", "Colors", "FileIO", "FixedPointNumbers", "FreeTypeAbstraction", "GLFW", "GeometryBasics", "LinearAlgebra", "Markdown", "MeshIO", "ModernGL", "Observables", "Printf", "Serialization", "ShaderAbstractions", "StaticArrays"]
-git-tree-sha1 = "2ddb89b95c3061f4e9bcf55b7e71454fed5eadfc"
-uuid = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
-version = "0.1.4"
+[[GR]]
+deps = ["Base64", "DelimitedFiles", "HTTP", "JSON", "LinearAlgebra", "Printf", "Random", "Serialization", "Sockets", "Test", "UUIDs"]
+git-tree-sha1 = "247adbd2b33c0c4b42efa20d1e807acf6312145f"
+uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
+version = "0.50.1"
[[GeometryBasics]]
deps = ["IterTools", "LinearAlgebra", "StaticArrays", "StructArrays", "Tables"]
@@ -286,52 +108,28 @@ git-tree-sha1 = "4ab7ad8996fc164a80cf18a7ef3a0b9e3465ae8e"
uuid = "5c1252a2-5f33-56bf-86c9-59e7332b4326"
version = "0.2.9"
-[[Graphics]]
-deps = ["Colors", "LinearAlgebra", "NaNMath"]
-git-tree-sha1 = "45d684ead5b65c043ad46bd5be750d61c39d7ef8"
-uuid = "a2bd30eb-e257-5431-a919-1863eab51364"
-version = "1.0.2"
+[[GeometryTypes]]
+deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "StaticArrays"]
+git-tree-sha1 = "34bfa994967e893ab2f17b864eec221b3521ba4d"
+uuid = "4d00f742-c7ba-57c2-abde-4428a4b178cb"
+version = "0.8.3"
-[[ImageCore]]
-deps = ["Colors", "FixedPointNumbers", "Graphics", "MappedArrays", "MosaicViews", "OffsetArrays", "PaddedViews", "Reexport", "Requires"]
-git-tree-sha1 = "a652c05f8f374861580d420b420fddf3e2e84312"
-uuid = "a09fc81d-aa75-5fe9-8630-4744c3626534"
-version = "0.8.14"
-
-[[ImageMagick]]
-deps = ["FileIO", "ImageCore", "ImageMagick_jll", "InteractiveUtils", "Libdl", "Pkg", "Random"]
-git-tree-sha1 = "318342a5099a9c952b4de087344a2db831458c87"
-uuid = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
-version = "1.1.5"
-
-[[ImageMagick_jll]]
-deps = ["JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pkg", "Zlib_jll", "libpng_jll"]
-git-tree-sha1 = "1c0a2295cca535fabaf2029062912591e9b61987"
-uuid = "c73af94c-d91f-53ed-93a7-00f77d67a9d7"
-version = "6.9.10-12+3"
-
-[[IntelOpenMP_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "fb8e1c7a5594ba56f9011310790e03b5384998d6"
-uuid = "1d5cc7b8-4909-519e-a0f8-d0f5ad9712d0"
-version = "2018.0.3+0"
+[[HTTP]]
+deps = ["Base64", "Dates", "IniFile", "MbedTLS", "Sockets"]
+git-tree-sha1 = "eca61b35cdd8cd2fcc5eec1eda766424a995b02f"
+uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
+version = "0.8.16"
+
+[[IniFile]]
+deps = ["Test"]
+git-tree-sha1 = "098e4d2c533924c921f9f9847274f2ad89e018b8"
+uuid = "83e8ac13-25f8-5344-8a64-a9f2b223428f"
+version = "0.5.0"
[[InteractiveUtils]]
deps = ["Markdown"]
uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
-[[Interpolations]]
-deps = ["AxisAlgorithms", "LinearAlgebra", "OffsetArrays", "Random", "Ratios", "SharedArrays", "SparseArrays", "StaticArrays", "WoodburyMatrices"]
-git-tree-sha1 = "3af735234d9b1ff9ff1af89875735cd9549c0c5f"
-uuid = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
-version = "0.12.9"
-
-[[IntervalSets]]
-deps = ["Dates", "EllipsisNotation", "Statistics"]
-git-tree-sha1 = "3b1cef135bc532b3c3401b309e1b8a2a2ba26af5"
-uuid = "8197267c-284f-5f27-9208-e0e47529a953"
-version = "0.5.1"
-
[[IterTools]]
git-tree-sha1 = "05110a2ab1fc5f932622ffea2a003221f4782c18"
uuid = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
@@ -348,18 +146,6 @@ git-tree-sha1 = "b34d7cef7b337321e97d22242c3c2b91f476748e"
uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
version = "0.21.0"
-[[JpegTurbo_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "b007cedc10bb017cdae3af0062fe4dd29b483c70"
-uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8"
-version = "2.0.1+0"
-
-[[KernelDensity]]
-deps = ["Distributions", "FFTW", "Interpolations", "Optim", "StatsBase", "Test"]
-git-tree-sha1 = "c1048817fe5711f699abc8fabd47b1ac6ba4db04"
-uuid = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
-version = "0.5.1"
-
[[LAME_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "221cc8998b9060677448cbb6375f00032554c4fd"
@@ -379,81 +165,33 @@ version = "1.8.1+1"
[[Libdl]]
uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
-[[Libgcrypt_jll]]
-deps = ["Libdl", "Libgpg_error_jll", "Pkg"]
-git-tree-sha1 = "7efb7f5e75a0f7323cce366d3e44a2d225c5617a"
-uuid = "d4300ac3-e22c-5743-9152-c294e39db1e4"
-version = "1.8.5+1"
-
-[[Libglvnd_jll]]
-deps = ["Libdl", "Pkg", "Xorg_libX11_jll", "Xorg_libXext_jll"]
-git-tree-sha1 = "30a990af9ce535bcba54d1f23f071a7e714f861e"
-uuid = "7e76a0d4-f3c7-5321-8279-8d96eeed0f29"
-version = "1.3.0+1"
-
-[[Libgpg_error_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "334284a62d6b8592099d28b78c76aefc266c03a2"
-uuid = "7add5ba3-2f88-524e-9cd5-f83b8a55f7b8"
-version = "1.36.0+1"
-
-[[Libiconv_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "e5256a3b0ebc710dbd6da0c0b212164a3681037f"
-uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531"
-version = "1.16.0+2"
-
-[[Libtiff_jll]]
-deps = ["JpegTurbo_jll", "Libdl", "Pkg", "Zlib_jll", "Zstd_jll"]
-git-tree-sha1 = "1fe8c3608dfe7bdec81d018de1cc66e959016e8c"
-uuid = "89763e89-9b03-5906-acba-b20f662cd828"
-version = "4.1.0+0"
-
-[[LineSearches]]
-deps = ["LinearAlgebra", "NLSolversBase", "NaNMath", "Parameters", "Printf", "Test"]
-git-tree-sha1 = "54eb90e8dbe745d617c78dee1d6ae95c7f6f5779"
-uuid = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
-version = "7.0.1"
-
[[LinearAlgebra]]
deps = ["Libdl"]
uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-[[Loess]]
-deps = ["Distances", "Statistics"]
-git-tree-sha1 = "67b1bc5f42144fb83b065b8a8e117e56ec10e601"
-uuid = "4345ca2d-374a-55d4-8d30-97f9976e7612"
-version = "0.5.1"
-
[[Logging]]
uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
-[[MKL_jll]]
-deps = ["IntelOpenMP_jll", "Libdl", "Pkg"]
-git-tree-sha1 = "720629cc8cbd12c146ca01b661fd1a6cf66e2ff4"
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[[Markdown]]
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+[[MbedTLS]]
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+git-tree-sha1 = "426a6978b03a97ceb7ead77775a1da066343ec6e"
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+
+[[MbedTLS_jll]]
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+
+[[Measures]]
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[[Missings]]
deps = ["DataAPI"]
@@ -464,68 +202,22 @@ version = "0.4.3"
[[Mmap]]
uuid = "a63ad114-7e13-5084-954f-fe012c677804"
-[[ModernGL]]
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[[OpenSSL_jll]]
deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "d2a6f25262d568b5a7e454cf7ff5066a79d16c7d"
+git-tree-sha1 = "7aaaded15bf393b5f34c2aad5b765c18d26cb495"
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-[[Optim]]
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+version = "1.1.1+4"
[[Opus_jll]]
deps = ["Libdl", "Pkg"]
@@ -538,30 +230,6 @@ git-tree-sha1 = "12ce190210d278e12644bcadf5b21cbdcf225cd3"
uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
version = "1.2.0"
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-[[Packing]]
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[[Parsers]]
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git-tree-sha1 = "f0abb338b4d00306500056a3fd44c221b8473ef2"
@@ -572,28 +240,28 @@ version = "1.0.4"
deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+[[PlotThemes]]
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+
[[PlotUtils]]
deps = ["ColorSchemes", "Colors", "Dates", "Printf", "Random", "Reexport", "Statistics"]
git-tree-sha1 = "59ec24a0c96c513533e488dff1433df1bd3d6b9f"
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-[[PositiveFactorizations]]
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+[[Plots]]
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[[Printf]]
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[[REPL]]
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uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
@@ -602,10 +270,16 @@ uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
deps = ["Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-[[Ratios]]
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+[[RecipesBase]]
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+[[RecipesPipeline]]
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[[Reexport]]
deps = ["Pkg"]
@@ -619,51 +293,18 @@ git-tree-sha1 = "d37400976e98018ee840e0ca4f9d20baa231dc6b"
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[[SHA]]
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[[Serialization]]
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[[Showoff]]
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[[Sockets]]
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@@ -677,12 +318,6 @@ version = "0.3.1"
deps = ["LinearAlgebra", "Random"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-[[SpecialFunctions]]
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[[StaticArrays]]
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git-tree-sha1 = "5c06c0aeb81bef54aed4b3f446847905eb6cbda0"
@@ -704,34 +339,12 @@ git-tree-sha1 = "a6102b1f364befdb05746f386b67c6b7e3262c45"
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[[TableTraits]]
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git-tree-sha1 = "b1ad568ba658d8cbb3b892ed5380a6f3e781a81e"
@@ -752,127 +365,14 @@ uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
deps = ["Random", "SHA"]
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[[Unicode]]
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-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "b25b0fb10176c42e9a5a20e1f40d570ac0288d4e"
-uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
-version = "1.4.5+0"
+version = "1.2.11+14"
[[libass_jll]]
deps = ["Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "Libdl", "Pkg", "Zlib_jll"]
@@ -886,12 +386,6 @@ git-tree-sha1 = "480c7ed04f68ea3edd4c757f5db5b6a0a4e0bd99"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "0.1.6+2"
-[[libpng_jll]]
-deps = ["Libdl", "Pkg", "Zlib_jll"]
-git-tree-sha1 = "594cb058723c13941cf463fd09e5859499594f50"
-uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
-version = "1.6.37+3"
-
[[libvorbis_jll]]
deps = ["Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "6a66f65b5275dfa799036c8a3a26616a0a271c4a"
diff --git a/Project.toml b/Project.toml
index 367f642..2f5f8ae 100644
--- a/Project.toml
+++ b/Project.toml
@@ -5,7 +5,7 @@ version = "0.1.0"
[deps]
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
StaticKernels = "4c63dfa8-a427-4548-bd2f-4c19e87a7dc7"
diff --git a/src/DualTVDD.jl b/src/DualTVDD.jl
index cdbf66d..af27db1 100644
--- a/src/DualTVDD.jl
+++ b/src/DualTVDD.jl
@@ -1,15 +1,12 @@
module DualTVDD
-
-
include("common.jl")
include("problems.jl")
include("chambolle.jl")
include("dualtvdd.jl")
include("projgrad.jl")
-
-using Makie: heatmap
+using Plots
function run()
#g = [0. 2; 1 0.]
@@ -25,9 +22,9 @@ function run()
display(norm(B))
- md = DualTVDD.DualTVL1ROFOpProblem(g, B, λ)
- ctx = DualTVDD.init(md, DualTVDD.ChambolleAlgorithm())
- ctx2 = DualTVDD.init(md, DualTVDD.ProjGradAlgorithm(τ=1/sqrt(8)/norm(B)))
+ prob = DualTVDD.DualTVL1ROFOpProblem(g, B, λ)
+ ctx = DualTVDD.init(DualTVDD.ChambolleAlgorithm(prob))
+ ctx2 = DualTVDD.init(DualTVDD.ProjGradAlgorithm(prob, τ=1/sqrt(8)/norm(B)))
#scene = vbox(
# heatmap(ctx.s, colorrange=(0,1), colormap=:gray, scale_plot=false, show_axis=false),
@@ -44,13 +41,13 @@ function run()
#sleep(0.2)
end
display(copy(ctx.p))
- display(copy(recover_u!(ctx)))
+ #display(copy(recover_u!(ctx)))
println(energy(ctx))
println()
display(copy(ctx2.p))
- display(copy(recover_u!(ctx2)))
+ #display(copy(recover_u!(ctx2)))
println(energy(ctx2))
ctx, ctx2
@@ -58,43 +55,89 @@ function run()
#yield()
end
+function alg_energy(alg, niter)
+ print("run ...")
+ res = Float64[]
+ (p, ctx) = iterate(alg)
+ push!(res, energy(ctx))
+ for i in 1:niter
+ (p, ctx) = iterate(alg, ctx)
+ push!(res, energy(ctx))
+ end
+ println(" finished")
+ return res
+end
+
+function alg_error(alg, pmin, niter, ninner=1)
+ print("run ...")
+ res = Float64[]
+ (p, ctx) = iterate(alg)
+ push!(res, error(ctx.p, pmin, ctx.algorithm.problem))
+ for i in 1:niter
+ for j in 1:ninner
+ (p, ctx) = iterate(alg, ctx)
+ end
+ push!(res, error(ctx.p, pmin, ctx.algorithm.problem))
+ end
+ println(" finished")
+ return res
+end
+
+function calc_energy(prob, niter)
+ alg_ref = DualTVDD.ChambolleAlgorithm(prob)
+ ctx = init(alg_ref)
+ for i in 1:niter
+ (p, ctx) = iterate(alg_ref, ctx)
+ end
+ return energy(ctx), p
+end
+
function rundd()
- λ = 1/2
+ λ = 2.5
β = 0
- f = zeros(10,10)
- f[1] = 1
+ #f = zeros(100,100)
+ #f[1] = 1
#f = [0. 2; 1 0.]
- A = 0. * rand(length(f), length(f))
- A .+= diagm(ones(length(f)))
- B = inv(A'*A + β*I)
+ #A = 0. * rand(length(f), length(f))
+ #A .+= diagm(ones(length(f)))
+ #B = inv(A'*A + β*I)
- g = similar(f)
- vec(g) .= A' * vec(f)
+ #g = similar(f)
+ #vec(g) .= A' * vec(f)
+
+ g = rand(50, 50)
+
+ prob = DualTVDD.DualTVL1ROFOpProblem(g, I, λ)
- prob = DualTVDD.DualTVL1ROFOpProblem(g, B, λ)
- alg_dd = DualTVDD.DualTVDDAlgorithm(prob, M=(2,2), overlap=(2,2), σ=0.25)
alg_ref = DualTVDD.ChambolleAlgorithm(prob)
+ alg_dd = DualTVDD.DualTVDDAlgorithm(prob, M=(2,2), overlap=(4,4))
+ alg_dd2 = DualTVDD.DualTVDDAlgorithm(prob, M=(2,2), overlap=(4,4), parallel=false)
- (p, ctx) = iterate(alg_ref)
- for i in 1:100000
- (p, ctx) = iterate(alg_ref, ctx)
- end
- display(ctx.p)
- display(recover_u(p, prob))
+ n = 1000
+ ref_energy, pmin = calc_energy(prob, 100000)
- (p, ctx) = iterate(alg_dd)
- for i in 1:1000
- (p, ctx) = iterate(alg_dd, ctx)
- end
- display(ctx.p)
- #display(ctx.subctx[1].algorithm.problem.g)
- #display(ctx.subctx[2].algorithm.problem.g)
- display(ctx.subctx[1].p)
- display(ctx.subctx[2].p)
- display(recover_u(p, prob))
+ lognan(x) = x > 0 ? x : NaN
+
+ #y = [
+ # lognan.(alg_energy(alg_ref, n) .- ref_energy),
+ # lognan.(alg_energy(alg_dd, n) .- ref_energy),
+ # lognan.(alg_energy(alg_dd2, n) .- ref_energy),
+ # ]
+ y = [
+ lognan.(alg_error(alg_ref, pmin, n, 10)),
+ lognan.(alg_error(alg_dd, pmin, n)),
+ lognan.(alg_error(alg_dd2, pmin, n)),
+ ]
+
+ plot(y, xaxis=:log, yaxis=:log)
+
+
+ #display(energy(ctx))
+ #display(ctx.p)
+ #display(recover_u(p, prob))
#println(energy(ctx))
#println(energy(ctx2))
@@ -139,40 +182,46 @@ function run3()
ctx, ctx2
end
+function energy(ctx::Union{DualTVDDState,ProjGradState,ChambolleState})
+ return energy(ctx.p, ctx.algorithm.problem)
+end
+
+function error(p, pmin, prob::DualTVL1ROFOpProblem)
+ return energy_norm(p .- pmin, prob) / energy(pmin, prob)
+end
-function energy(ctx::Union{DualTVDDState,ProjGradState})
- d = ndims(ctx.p)
+function energy(p, prob::DualTVL1ROFOpProblem)
+ d = ndims(p)
@inline kfΛ(w) = @inbounds divergence(w)
kΛ = Kernel{ntuple(_->-1:1, d)}(kfΛ)
- v = similar(ctx.g)
+ v = similar(prob.g)
- # v = div(p) + A'*f
- map!(kΛ, v, extend(ctx.p, StaticKernels.ExtensionNothing()))
- v .+= ctx.g
+ # v = div(p) + g
+ map!(kΛ, v, extend(p, StaticKernels.ExtensionNothing()))
+ v .+= prob.g
#display(v)
# |v|_B^2 / 2
- u = ctx.B * vec(v)
+ u = prob.B * vec(v)
return sum(u .* vec(v)) / 2
end
-function energy(ctx::ChambolleState)
- d = ndims(ctx.p)
+function energy_norm(p, prob::DualTVL1ROFOpProblem)
+ d = ndims(p)
@inline kfΛ(w) = @inbounds divergence(w)
kΛ = Kernel{ntuple(_->-1:1, d)}(kfΛ)
- v = similar(ctx.problem.g)
+ v = similar(prob.g)
- # v = div(p) + g
- map!(kΛ, v, extend(ctx.p, StaticKernels.ExtensionNothing()))
- v .+= ctx.problem.g
+ # v = div(p)
+ map!(kΛ, v, extend(p, StaticKernels.ExtensionNothing()))
#display(v)
# |v|_B^2 / 2
- u = ctx.problem.B * vec(v)
+ u = prob.B * vec(v)
return sum(u .* vec(v)) / 2
end
diff --git a/src/chambolle.jl b/src/chambolle.jl
index 0edc303..0a183cd 100644
--- a/src/chambolle.jl
+++ b/src/chambolle.jl
@@ -18,15 +18,14 @@ struct ChambolleAlgorithm{P} <: Algorithm{P}
"fixed point inertia parameter"
τ::Float64
+ "anisotropic projection"
+ anisotropic::Bool
- function ChambolleAlgorithm(problem; τ)
- return new{typeof(problem)}(problem, τ)
+ function ChambolleAlgorithm(problem; τ=inv(8 * normB(problem)), anisotropic=false)
+ return new{typeof(problem)}(problem, τ, anisotropic)
end
end
-ChambolleAlgorithm(problem::DualTVL1ROFOpProblem) =
- ChambolleAlgorithm(problem, τ=inv(8 * normB(problem)))
-
struct ChambolleState{A,T,R,S,Sv,K1,K2} <: State
algorithm::A
@@ -49,12 +48,9 @@ struct ChambolleState{A,T,R,S,Sv,K1,K2} <: State
k2::K2
end
-function proj(ctx::ChambolleState, x, s, λ)
- @inbounds iszero(λ) && return zero(p)
- return @inbounds p ./ (1 + norm(sgrad) / md.λ[sw.position])
-end
+projnorm(v, anisotropic) = anisotropic ? abs.(v) : norm(v)
-function Base.iterate(alg::ChambolleAlgorithm{<:DualTVL1ROFOpProblem})
+function init(alg::ChambolleAlgorithm{<:DualTVL1ROFOpProblem})
g = alg.problem.g
λ = alg.problem.λ
d = ndims(g)
@@ -71,18 +67,19 @@ function Base.iterate(alg::ChambolleAlgorithm{<:DualTVL1ROFOpProblem})
@inline function kf2(sw)
@inbounds iszero(λ[sw.position]) && return zero(p[sw.position])
sgrad = alg.τ * gradient(sw)
- return @inbounds (p[sw.position] + sgrad) / (1 + norm(sgrad) / λ[sw.position])
+ return @inbounds (p[sw.position] + sgrad) ./ (1 .+ projnorm(sgrad, alg.anisotropic) ./ λ[sw.position])
end
k2 = Kernel{ntuple(_->0:1, d)}(kf2)
- return (p, ChambolleState(alg, p, r, s, rv, sv, k1, k2))
+ return ChambolleState(alg, p, r, s, rv, sv, k1, k2)
end
function reset!(ctx::ChambolleState)
fill!(ctx.p, zero(eltype(ctx.p)))
end
-function Base.iterate(alg::ChambolleAlgorithm{<:DualTVL1ROFOpProblem}, ctx)
+function step!(ctx::ChambolleState)
+ alg = ctx.algorithm
# r = div(p) + g
map!(ctx.k1, ctx.r, ctx.p)
# s = B * r
@@ -90,34 +87,24 @@ function Base.iterate(alg::ChambolleAlgorithm{<:DualTVL1ROFOpProblem}, ctx)
# p = (p + τ*grad(s)) / (1 + τ/λ|grad(s)|)
map!(ctx.k2, ctx.p, ctx.s)
- return (ctx.p, ctx)
+ return ctx
end
-recover_u(ctx::ChambolleState) = recover_u(ctx.p, ctx.problem)
+fetch(ctx::ChambolleState) =
+ recover_u(ctx.p, ctx.algorithm.problem)
-#
-#function solve(md::ROFProblem, alg::Chambolle;
-# maxiters = 1000,
-# save_log = false)
-#
-# p = zeros(ndims(g), size(g)...)
-# q = zero(p)
-# s = zero(g)
-# div = div_op(p)
-# grad = grad_op(g)
-# ctx = ChambolleState(p, q, s, div, grad)
-#
-# log = Log()
-#
-# for i in 1:maxiters
-# step!(ctx, md, alg)
-#
-# if save_log
-# recover_u!(ctx, md, alg)
-# save_log && push!(log.energy, energy2(md, ctx.s))
-# end
-# end
-#
-# recover_u!(ctx, md, alg)
-# return Solution(ctx.s, md, alg, ctx, log)
-#end
+function recover_u(p, md::DualTVL1ROFOpProblem)
+ d = ndims(md.g)
+ u = similar(md.g)
+ v = similar(md.g)
+
+ @inline kfΛ(w) = @inbounds divergence(w)
+ kΛ = Kernel{ntuple(_->-1:1, d)}(kfΛ)
+
+ # v = div(p) + A'*f
+ map!(kΛ, v, extend(p, StaticKernels.ExtensionNothing())) # extension: nothing
+ v .+= md.g
+ # u = B * v
+ mul!(vec(u), md.B, vec(v))
+ return u
+end
diff --git a/src/common.jl b/src/common.jl
index 3643599..29a2d9e 100644
--- a/src/common.jl
+++ b/src/common.jl
@@ -1,25 +1,6 @@
using StaticArrays
using StaticKernels
-# Solver Interface Notes:
-#
-# - a concrete subtype `<:Problem` describes one model type and each instance
-# represents a fully specified problem, i.e. including data and model parameters
-# - a concrete subtype `<:Algorithm` describes an algorithm, constructed from a
-# model and algoritm parameters as keyword arguments
-# - a concrete subtype `<:Context` contains preallocated data for the
-# algorithm
-#
-# - `init(::Problem, ::Algorithm)::Context` initializes the algorithm context
-# - `iterate!(::Context)::Union{Context,Nothing}` performs one non-allocating step
-#
-# - iterating over the same context must lead to the same deterministic
-# sequence of iterates
-# - `run(::Context)` must continue on from the given context
-# - `(<:Algorithm)(...)` constructors must accept keyword arguments only
-# - types `<:Context` satisfy a stateful iterator interface, returning the
-# current context at each step of the algorithm
-
"""
<:Problem
@@ -33,8 +14,8 @@ abstract type Problem end
(<:Algorithm)(::Problem; params...)
An algorithm represents a fully specified iterative process of usually infite
-or a-priori unknown finite length, producing iterates that approximate the
-solution to some problem (see `::Problem`).
+or a-priori fixed but unknown finite length, producing iterates that
+approximate the solution to some problem (see `::Problem`).
The hierarchy of abstract types `<:Algorithm` is based on the algorithm
interface (e.g. accepted parameters) and the specific numerical scheme.
@@ -44,44 +25,35 @@ algorithm. An instance is a complete specification of the algorithm with all
its inputs and should guarantee to produce a deterministic sequence of iterates
(randomized algorithms should use a seeded pseudorandom number generator).
-An algorithm needs to implement
+An concrete algorithm type needs to implement at least
+
+ - `state = init(::Algorithm)` allocates and initializes the algorithm state.
+ - `state = step!(::State)` performs one iteration of the algorithm by
+ updating `state`. This method should not allocate dynamic memory.
+ - `solution = fetch(::State)` fetches the solution in a format specified by
+ the problem. In some cases this can be a non-trivial operation if the
+ algorithm uses a different internal representation.
- - `(x0, state) = Base.iterate(::Algorithm)` allocates and initializes the
- algorithm state and returns the initial iterate `x0`.
- - `(x, state) = Base.iterate(::Algorithm, state)` performs one iteration of the
- algorithm by updating `state` and returning the updated iterate `x`. This
- method should not allocate dynamic memory.
+Depending on its supertype algorithm interface additional methods might need to
+be implemented.
"""
abstract type Algorithm{P<:Problem} end
-Base.IteratorSize(::Type{<:Algorithm}) = Base.SizeUnknown()
-
-function execute(alg::Algorithm; maxiters=nothing)
- k = 0
- y = iterate(alg)
- @assert !isnothing(y)
- (x, state) = y
- while true
- k += 1
- k > maxiters && break
- y = iterate(alg, state)
- isnothing(y) && break
- (x, state) = y
- end
- return x
-end
+"""
+ <:State
+Algorithm state and storage containing sufficient information to act as a
+checkpoint for continuing the corresponding algorithm at that point.
+"""
abstract type State end
+function init end
+function step! end
+function fetch end
-"helper struct to prevent allocations"
-struct AppliedLinearKernel{K,A}
- kernel::K
- data::A
-end
-
-compute!(dst, op::AppliedLinearKernel) = map!(dst, op.kernel, op.a)
+Base.intersect(a::CartesianIndices{d}, b::CartesianIndices{d}) where d =
+ CartesianIndices(intersect.(a.indices, b.indices))
@generated function gradient(w::StaticKernels.Window{<:Any,N}) where N
i0 = ntuple(_->0, N)
diff --git a/src/dualtvdd.jl b/src/dualtvdd.jl
index 456ebae..b54df22 100644
--- a/src/dualtvdd.jl
+++ b/src/dualtvdd.jl
@@ -5,10 +5,14 @@ struct DualTVDDAlgorithm{P,d} <: Algorithm{P}
M::NTuple{d,Int}
"overlap in pixels per dimension"
overlap::NTuple{d,Int}
- "inertia parameter"
+ "use the non-sequential inertia update"
+ parallel::Bool
+ "inertia parameter (only when parallel)"
σ::Float64
- function DualTVDDAlgorithm(problem; M, overlap, σ)
- return new{typeof(problem), length(M)}(problem, M, overlap, σ)
+ "number of inner iterations"
+ ninner::Int
+ function DualTVDDAlgorithm(problem; M, overlap, parallel=true, σ=1/4, ninner=10)
+ return new{typeof(problem), length(M)}(problem, M, overlap, parallel, σ, ninner)
end
end
@@ -57,41 +61,57 @@ function Base.iterate(alg::DualTVDDAlgorithm{<:DualTVL1ROFOpProblem})
return p, DualTVDDState(alg, p, q, subax, subctx)
end
+function intersectin(a, b)
+ c = intersect(a, b)
+ az = first(a) - one(eltype(a))
+ bz = first(b) - one(eltype(b))
+ return (c, c .- az, c .- bz)
+end
+
function Base.iterate(alg::DualTVDDAlgorithm{<:DualTVL1ROFOpProblem}, ctx)
- σ = ctx.algorithm.σ
+ # σ = 1 takes care of sequential updates
+ σ = alg.parallel ? ctx.algorithm.σ : 1.
d = ndims(ctx.p)
ax = axes(ctx.p)
overlap = ctx.algorithm.overlap
# call run! on each cell (this can be threaded)
for (i, sax) in pairs(ctx.subax)
+ li = LinearIndices(ctx.subax)[i]
sg = ctx.subctx[i].algorithm.problem.g # julia-bug workaround
sq = ctx.q[i] # julia-bug workaround
- #println("# subax $sax")
-
sg .= alg.problem.g[sax...]
- sq .= (1 .- theta.(Ref(ax), Ref(sax), Ref(overlap), CartesianIndices(sax))) .* ctx.p[sax...]
+ if alg.parallel
+ sq .= (1 .- theta.(Ref(ax), Ref(sax), Ref(overlap), CartesianIndices(sax))) .* ctx.p[sax...]
+ else
+ sq .= Ref(zero(eltype(sq)))
+ # contributions from previous domains
+ for (lj, saxj) in enumerate(ctx.subax)
+ ids, idsi, idsj = intersectin(CartesianIndices(sax), CartesianIndices(saxj))
+ if lj < li
+ sq[idsi] .+= ctx.subctx[lj].p[idsj]
+ elseif lj > li
+ sq[idsi] .+= theta.(Ref(ax), Ref(saxj), Ref(overlap), ids) .* ctx.p[ids]
+ end
+ end
+ end
@inline kfΛ(pw) = @inbounds sg[pw.position] + divergence(pw)
kΛ = Kernel{ntuple(_->-1:1, d)}(kfΛ)
map!(kΛ, sg, sq)
- for j in 1:10
+ for j in 1:alg.ninner
(_, ctx.subctx[i]) = iterate(ctx.subctx[i].algorithm, ctx.subctx[i])
end
- #display(ctx.subctx[i].algorithm.problem.g .- alg.problem.g[sax...])
- #display(ctx.subctx[i].p)
end
- #display(ctx.p)
# aggregate (not thread-safe!)
ctx.p .*= 1 - σ
for (i, sax) in pairs(ctx.subax)
ctx.p[sax...] .+= σ .* ctx.subctx[i].p
end
- #display(ctx.p)
return ctx.p, ctx
end
diff --git a/src/problems.jl b/src/problems.jl
index 7e7fc7d..6f6a81c 100644
--- a/src/problems.jl
+++ b/src/problems.jl
@@ -18,19 +18,3 @@ DualTVL1ROFOpProblem(g, B, λ::Real) = DualTVL1ROFOpProblem(g, B, fill!(similar(
normB(p::DualTVL1ROFOpProblem{<:Any,<:UniformScaling}) = p.B.λ * sqrt(length(p.g))
normB(p::DualTVL1ROFOpProblem) = norm(p.B)
-
-function recover_u(p, md::DualTVL1ROFOpProblem)
- d = ndims(md.g)
- u = similar(md.g)
- v = similar(md.g)
-
- @inline kfΛ(w) = @inbounds divergence(w)
- kΛ = Kernel{ntuple(_->-1:1, d)}(kfΛ)
-
- # v = div(p) + A'*f
- map!(kΛ, v, extend(p, StaticKernels.ExtensionNothing())) # extension: nothing
- v .+= md.g
- # u = B * v
- mul!(vec(u), md.B, vec(v))
- return u
-end
diff --git a/src/projgrad.jl b/src/projgrad.jl
index 1de6503..6bbc2ed 100644
--- a/src/projgrad.jl
+++ b/src/projgrad.jl
@@ -30,13 +30,13 @@ struct ProjGradState{A,Tp,Wv,R,S,K1,K2}
k2::K2
end
-function Base.iterate(alg::ProjGradAlgorithm{<:DualTVL1ROFOpProblem})
+function init(alg::ProjGradAlgorithm{<:DualTVL1ROFOpProblem})
g = alg.problem.g
d = ndims(g)
ax = axes(g)
p = extend(zeros(SVector{d,Float64}, ax), StaticKernels.ExtensionNothing())
- g = extend(g, StaticKernels.ExtensionNothing())
+ ge = extend(g, StaticKernels.ExtensionNothing())
rv = zeros(length(g))
sv = zeros(length(g))
@@ -46,7 +46,7 @@ function Base.iterate(alg::ProjGradAlgorithm{<:DualTVL1ROFOpProblem})
z = zero(CartesianIndex{d})
- @inline kf1(pw) = @inbounds -divergence(pw) - g[pw.position]
+ @inline kf1(pw) = @inbounds -divergence(pw) - ge[pw.position]
k1 = Kernel{ntuple(_->-1:1, d)}(kf1)
@inline function kf2(pw, sw)
@@ -55,10 +55,11 @@ function Base.iterate(alg::ProjGradAlgorithm{<:DualTVL1ROFOpProblem})
end
k2 = Kernel{ntuple(_->0:1, d)}(kf2)
- return (p, ProjGradState(alg, p, rv, sv, r, s, k1, k2))
+ return ProjGradState(alg, p, rv, sv, r, s, k1, k2)
end
-function Base.iterate(alg::ProjGradAlgorithm{<:DualTVL1ROFOpProblem}, ctx)
+function step!(ctx::ProjGradState)
+ alg = ctx.algorithm
# r = Λ*p - g
map!(ctx.k1, ctx.r, ctx.p)
# s = B * r
@@ -66,7 +67,7 @@ function Base.iterate(alg::ProjGradAlgorithm{<:DualTVL1ROFOpProblem}, ctx)
# p = proj(p - λΛ's)
map!(ctx.k2, ctx.p, ctx.p, ctx.s)
- return (ctx.p, ctx)
+ return ctx
end
-recover_u(ctx::ProjGradState) = recover_u(ctx.p, ctx.problem)
+fetch(ctx::ProjGradState) = recover_u(ctx.p, ctx.algorithm.problem)
diff --git a/test/runtests.jl b/test/runtests.jl
index 9e8d0d0..fa69c23 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,13 +1,18 @@
-using Test
+using Test, BenchmarkTools
using LinearAlgebra
using DualTVDD:
DualTVL1ROFOpProblem, ProjGradAlgorithm, ChambolleAlgorithm,
- init, step!, recover_u
+ init, step!, fetch
g = Float64[0 2; 1 0]
prob = DualTVL1ROFOpProblem(g, I, 1e-10)
-for alg in (ProjGradAlgorithm(prob, τ=1/8), ChambolleAlgorithm(prob))
- its = collect(Iterators.take(alg, 100))
- @test recover_u(last(its)) ≈ g
+@testset for alg in (ProjGradAlgorithm(prob, τ=1/8), ChambolleAlgorithm(prob))
+ ctx = init(alg)
+ @test 0 == @ballocated step!($ctx)
+ for i in 1:100
+ step!(ctx)
+ end
+ u = fetch(ctx)
+ @test u ≈ g
end
--
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