Interface upadte: contraction complexity (#67)

* use `contraction_complexity` and deprecate `timespacereadwrite_complexity`

* clean up pluto notebook building

* bump version

* update

* fix-cuda onehot

Author: GiggleLiu

Project.toml

@@ -1,7 +1,7 @@
 name = "GenericTensorNetworks"
 uuid = "3521c873-ad32-4bb4-b63d-f4f178f42b49"
 authors = ["GiggleLiu <cacate0129@gmail.com> and contributors"]
-version = "1.3.4"
+version = "1.3.5"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

docs/Project.toml

@@ -5,5 +5,4 @@ GenericTensorNetworks = "3521c873-ad32-4bb4-b63d-f4f178f42b49"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
-PlutoStaticHTML = "359b1769-a58e-495b-9770-312e911026ad"
 Polynomials = "f27b6e38-b328-58d1-80ce-0feddd5e7a45"

docs/make.jl

@@ -3,7 +3,6 @@ using GenericTensorNetworks
 using GenericTensorNetworks: TropicalNumbers, Polynomials, Mods, OMEinsum, OMEinsum.OMEinsumContractionOrders, LuxorGraphPlot
 using Documenter
 using DocThemeIndigo
-using PlutoStaticHTML
 using Literate
 
 for each in readdir(pkgdir(GenericTensorNetworks, "examples"))
@@ -14,22 +13,6 @@ for each in readdir(pkgdir(GenericTensorNetworks, "examples"))
     Literate.markdown(input_file, output_dir; name=each[1:end-3], execute=false)
 end
 
-let
-    """Run all Pluto notebooks (".jl" files) in `notebook_dir` and write outputs to HTML files."""
-    notebook_dir = joinpath(pkgdir(GenericTensorNetworks), "notebooks")
-    target_dir = joinpath(pkgdir(GenericTensorNetworks), "docs", "src", "notebooks")
-    cp(notebook_dir, target_dir; force=true)
-    @info "Building tutorials"
-    # Evaluate notebooks in the same process to avoid having to recompile from scratch each time.
-    # This is similar to how Documenter and Franklin evaluate code.
-    # Note that things like method overrides and other global changes may leak between notebooks!
-    use_distributed = true
-    output_format = documenter_output
-    bopts = BuildOptions(target_dir; use_distributed, output_format)
-    build_notebooks(bopts)
-    return nothing
-end
-
 indigo = DocThemeIndigo.install(GenericTensorNetworks)
 DocMeta.setdocmeta!(GenericTensorNetworks, :DocTestSetup, :(using GenericTensorNetworks); recursive=true)
 

docs/src/performancetips.md

@@ -28,11 +28,13 @@ It can remove all vertex tensors (vectors) before entering the contraction order
 
 The returned object `problem` contains a field `code` that specifies the tensor network with optimized contraction order.
 For an independent set problem, the optimal contraction time/space complexity is ``\sim 2^{{\rm tw}(G)}``, where ``{\rm tw(G)}`` is the [tree-width](https://en.wikipedia.org/wiki/Treewidth) of ``G``.
-One can check the time, space and read-write complexity with the [`timespacereadwrite_complexity`](@ref) function.
+One can check the time, space and read-write complexity with the [`contraction_complexity`](@ref) function.
 
 ```julia
-julia> timespacereadwrite_complexity(problem)
-(21.90683335864693, 17.0, 20.03588509836998)
+julia> contraction_complexity(problem)
+Time complexity (number of element-wise multiplications) = 2^20.568850503058382
+Space complexity (number of elements in the largest intermediate tensor) = 2^16.0
+Read-write complexity (number of element-wise read and write) = 2^18.70474460304404
 ```
 
 The return values are `log2` values of the number of multiplications, the number elements in the largest tensor during contraction and the number of read-write operations to tensor elements.
@@ -81,13 +83,17 @@ julia> graph = random_regular_graph(120, 3)
 
 julia> problem = IndependentSet(graph; optimizer=TreeSA(βs=0.01:0.1:25.0, ntrials=10, niters=10));
 
-julia> timespacereadwrite_complexity(problem)
-(20.856518235241687, 16.0, 18.88208476145812)
+julia> contraction_complexity(problem)
+Time complexity (number of element-wise multiplications) = 2^20.53277253647484
+Space complexity (number of elements in the largest intermediate tensor) = 2^16.0
+Read-write complexity (number of element-wise read and write) = 2^19.34699193791874
 
 julia> problem = IndependentSet(graph; optimizer=TreeSA(βs=0.01:0.1:25.0, ntrials=10, niters=10, nslices=5));
 
-julia> timespacereadwrite_complexity(problem)
-(21.134967710592804, 11.0, 19.84529401927876)
+julia> contraction_complexity(problem)
+Time complexity (number of element-wise multiplications) = 2^21.117277836449578
+Space complexity (number of elements in the largest intermediate tensor) = 2^11.0
+Read-write complexity (number of element-wise read and write) = 2^19.854965754099602
 ```
 
 In the second `IndependentSet` constructor, we slice over 5 degrees of freedom, which can reduce the space complexity by at most 5.

docs/src/ref.md

@@ -139,7 +139,6 @@ MergeGreedy
 show_graph
 show_gallery
 show_einsum
-spring_layout!
 
 diagonal_coupled_graph
 square_lattice_graph

examples/DominatingSet.jl

@@ -42,7 +42,7 @@ problem = DominatingSet(graph; optimizer=TreeSA());
 # otherwise, if ``v`` is in ``D``, it has a contribution ``x_v^{w_v}`` to the final result.
 # One can check the contraction time space complexity of a [`DominatingSet`](@ref) instance by typing:
 
-timespacereadwrite_complexity(problem)
+contraction_complexity(problem)
 
 # ## Solving properties
 

examples/IndependentSet.jl

@@ -51,9 +51,8 @@ problem = IndependentSet(graph; optimizer=TreeSA());
 # where ``{\rm tw(G)}`` is the [tree-width](https://en.wikipedia.org/wiki/Treewidth) of ``G`` (or `graph` in the code).
 # We can check the time, space and read-write complexities by typing
 
-timespacereadwrite_complexity(problem)
+contraction_complexity(problem)
 
-# The three return values are `log2` values of the the number of element-wise multiplication operations, the number elements in the largest tensor during contraction and the number of tensor element read-write operations.
 # For more information about how to improve the contraction order, please check the [Performance Tips](@ref).
 
 # ## Solution space properties

examples/MaximalIS.jl

@@ -41,9 +41,7 @@ problem = MaximalIS(graph; optimizer=TreeSA());
 # Its contraction time space complexity of a [`MaximalIS`](@ref) instance is no longer determined by the tree-width of the original graph ``G``.
 # It is often harder to contract this tensor network than to contract the one for regular independent set problem.
 
-timespacereadwrite_complexity(problem)
-
-# Results are `log2` values.
+contraction_complexity(problem)
 
 # ## Solving properties
 

examples/SetCovering.jl

@@ -42,7 +42,7 @@ problem = SetCovering(sets);
 # This tensor means if none of the sets containing element ``a`` are included, then this configuration is forbidden,
 # One can check the contraction time space complexity of a [`SetCovering`](@ref) instance by typing:
 
-timespacereadwrite_complexity(problem)
+contraction_complexity(problem)
 
 # ## Solving properties
 

examples/SetPacking.jl

@@ -43,7 +43,7 @@ problem = SetPacking(sets);
 # This tensor means if in a configuration, two sets contain the element ``a``, then this configuration is forbidden,
 # One can check the contraction time space complexity of a [`SetPacking`](@ref) instance by typing:
 
-timespacereadwrite_complexity(problem)
+contraction_complexity(problem)
 
 # ## Solving properties