QuEraComputing · GiggleLiu · Feb 8, 2022 · Feb 8, 2022 · Feb 8, 2022
diff --git a/README.md b/README.md
@@ -1,7 +1,7 @@
 # GraphTensorNetworks
 
 [![Build Status](https://github.com/Happy-Diode/GraphTensorNetworks.jl/workflows/CI/badge.svg)](https://github.com/Happy-Diode/GraphTensorNetworks.jl/actions)
-[![Coverage Status](https://coveralls.io/repos/github/Happy-Diode/GraphTensorNetworks.jl/badge.svg?branch=fix-test-coverage&t=rIJIK2)](https://coveralls.io/github/Happy-Diode/GraphTensorNetworks.jl?branch=fix-test-coverage)
+[![Coverage Status](https://coveralls.io/repos/github/Happy-Diode/GraphTensorNetworks.jl/badge.svg?branch=master&t=rIJIK2)](https://coveralls.io/github/Happy-Diode/GraphTensorNetworks.jl?branch=master)
 [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://psychic-meme-f4d866f8.pages.github.io/dev/)
 
 ## Installation

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -4,11 +4,20 @@ CurrentModule = GraphTensorNetworks
 
 # GraphTensorNetworks
 
+This package uses generic tensor network to compute properties of combinatorial problems defined on graph.
+The properties includes the size of the maximum set size, the number of sets of a given size and the enumeration of configurations of a given set size.
+
 ## Background knowledge
 
 Please check our paper ["Computing properties of independent sets by generic programming tensor networks"]().
-If you find our paper or software useful in your work, please cite us.
+If you find our paper or software useful in your work, please cite us:
+
+```bibtex
+(bibtex to be added)
+```
 
 ## Quick start
 
-You can find a good installation guide and a quick start in our [README](https://github.com/Happy-Diode/GraphTensorNetworks.jl).
+You can find a good installation guide and a quick start in our [README](https://github.com/Happy-Diode/GraphTensorNetworks.jl).
+
+A good example to start with is the [Independent set problem](@ref).
diff --git a/docs/src/ref.md b/docs/src/ref.md
@@ -17,17 +17,25 @@ set_packing
 
 #### Graph Problem Interfaces
 ```@docs
-generate_tensors
+GraphTensorNetworks.generate_tensors
 symbols
 flavors
 get_weights
 nflavor
 ```
 
 To subtype [`GraphProblem`](@ref), the new type must contain a `code` field to represent the (optimized) tensor network.
-Interfaces [`generate_tensors`](@ref), [`symbols`](@ref), [`flavors`](@ref) and [`get_weights`] are required.
+Interfaces [`GraphTensorNetworks.generate_tensors`](@ref), [`symbols`](@ref), [`flavors`](@ref) and [`get_weights`] are required.
 [`nflavor`] is optimal.
 
+#### Graph Problem Utilities
+```@docs
+is_independent_set
+mis_compactify!
+
+cut_size
+cut_assign
+```
 
 ## Properties
 ```@docs
@@ -81,13 +89,14 @@ MergeGreedy
 ## Others
 #### Graph
 ```@docs
-is_independent_set
-mis_compactify!
 show_graph
 
 diagonal_coupled_graph
 square_lattice_graph
-unitdisk_graph
+unit_disk_graph
+
+random_diagonal_coupled_graph
+random_square_lattice_graph
 ```
 
 One can also use `random_regular_graph` and `smallgraph` in [Graphs](https://github.com/JuliaGraphs/Graphs.jl) to build special graphs.

diff --git a/examples/Coloring/main.jl b/examples/Coloring/main.jl
@@ -0,0 +1,31 @@
+# # Coloring problem
+
+# !!! note
+#     This tutorial only covers the coloring problem specific features,
+#     It is recommended to read the [Independent set problem](@ref) tutorial too to know more about
+#     * how to optimize the tensor network contraction order,
+#     * what are the other graph properties computable,
+#     * how to select correct method to compute graph properties,
+#     * how to compute weighted graphs and handle open vertices.
+
+# ## Introduction
+using GraphTensorNetworks, Graphs
+
+# Please check the docstring of [`Coloring`](@ref) for the definition of the vertex cutting problem.
+@doc Coloring
+
+# In the following, we are going to defined a 3-coloring problem for the Petersen graph.
+
+graph = Graphs.smallgraph(:petersen)
+
+# We can visualize this graph using the following function
+rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
+
+locations = [[rot15(0.0, 1.0, i) for i=0:4]..., [rot15(0.0, 0.6, i) for i=0:4]...]
+
+show_graph(graph; locs=locations)
+
+# Then we define the cutting problem as
+problem = Coloring{3}(graph);
+
+# ## Solving properties
diff --git a/examples/IndependentSet/main.jl b/examples/IndependentSet/main.jl
@@ -22,24 +22,36 @@ problem = Independence(graph; optimizer=TreeSA(sc_weight=1.0, ntrials=10,
                          βs=0.01:0.1:15.0, niters=20, rw_weight=0.2),
                          simplifier=MergeGreedy());
 
+# The `optimizer` is for optimizing the contraction orders.
+# Here we use the local search based optimizer in [arXiv:2108.05665](https://arxiv.org/abs/2108.05665).
+# If no optimizer is specified, the default fast (in terms of the speed of searching contraction order)
+# but worst (in term of contraction complexity) [`GreedyMethod`](@ref) will be used.
+# `simplifier` is a preprocessing routine to speed up the `optimizer`.
+# Please check section [Tensor Network](@ref) for more details.
+# One can check the time, space and read-write complexity with the following function.
+
+timespacereadwrite_complexity(problem)
+
+# The return values are `log2` of the the number of iterations, the number elements in the max tensor and the number of read-write operations to tensor elements.
+
 
 # ## Solving properties
 
 # ### Maximum independent set size ``\alpha(G)``
-maximum_independent_set_size = solve(problem, SizeMax())
+maximum_independent_set_size = solve(problem, SizeMax())[]
 
 # ### Counting properties
 # ##### counting all independent sets
-count_all_independent_sets = solve(problem, CountingAll())
+count_all_independent_sets = solve(problem, CountingAll())[]
 
 # ##### counting independent sets with sizes ``\alpha(G)`` and ``\alpha(G)-1``
-count_max2_independent_sets = solve(problem, CountingMax(2))
+count_max2_independent_sets = solve(problem, CountingMax(2))[]
 
 # ##### independence polynomial
 # For the definition of independence polynomial, please check the docstring of [`Independence`](@ref) or this [wiki page](https://mathworld.wolfram.com/IndependencePolynomial.html).
 # There are 3 methods to compute a graph polynomial, `:finitefield`, `:fft` and `:polynomial`.
 # These methods are introduced in the docstring of [`GraphPolynomial`](@ref).
-independence_polynomial = solve(problem, GraphPolynomial(; method=:finitefield))
+independence_polynomial = solve(problem, GraphPolynomial(; method=:finitefield))[]
 
 # ### Configuration properties
 # ##### finding one maximum independent set (MIS)
@@ -72,4 +84,38 @@ imgs = ntuple(k->(context((k-1)/m, 0.0, 1.2/m, 1.0), show_graph(graph;
 Compose.set_default_graphic_size(18cm, 4cm); Compose.compose(context(), imgs...)
 
 # ##### enumeration of all IS configurations
-all_independent_sets = solve(problem, ConfigsAll())[]
+all_independent_sets = solve(problem, ConfigsAll())[]
+
+# To save/read a set of configuration to disk, one can type the following
+filename = tempname()
+
+save_configs(filename, all_independent_sets; format=:binary)
+
+loaded_sets = load_configs(filename; format=:binary, bitlength=10)
+
+# !!! note
+#     When loading data, one needs to provide the `bitlength` if the data is saved in binary format.
+#     Because the bitstring length is not stored.
+
+# ## Weights and open vertices
+# [`Independence`] accepts weights as a key word argument.
+# The following code computes the weighted MIS problem.
+problem = Independence(graph; weights=collect(1:10))
+
+max_config_weighted = solve(problem, SingleConfigMax())[]
+
+show_graph(graph; locs=locations, colors=
+          [iszero(max_config_weighted.c.data[i]) ? "white" : "red" for i=1:nv(graph)])
+
+# The following code computes the MIS tropical tensor (reference to be added) with open vertices 1 and 2.
+problem = Independence(graph; openvertices=[1,2,3])
+
+mis_tropical_tensor = solve(problem, SizeMax())
+
+# The MIS tropical tensor shows the MIS size under different configuration of open vertices.
+# It is useful in MIS tropical tensor analysis.
+# One can compatify this MIS-Tropical tensor by typing
+
+mis_compactify!(mis_tropical_tensor)
+
+# It will eliminate some entries having no contribution to the MIS size when embeding this local graph into a larger one.
diff --git a/examples/Matching/main.jl b/examples/Matching/main.jl
@@ -0,0 +1,31 @@
+# # Vertex matching problem
+
+# !!! note
+#     This tutorial only covers the vertex matching problem specific features,
+#     It is recommended to read the [Independent set problem](@ref) tutorial too to know more about
+#     * how to optimize the tensor network contraction order,
+#     * what are the other graph properties computable,
+#     * how to select correct method to compute graph properties,
+#     * how to compute weighted graphs and handle open vertices.
+
+# ## Introduction
+using GraphTensorNetworks, Graphs
+
+# Please check the docstring of [`Matching`](@ref) for the definition of the vertex matching problem.
+@doc Matching
+
+# In the following, we are going to defined a matching problem for the Petersen graph.
+
+graph = Graphs.smallgraph(:petersen)
+
+# We can visualize this graph using the following function
+rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
+
+locations = [[rot15(0.0, 1.0, i) for i=0:4]..., [rot15(0.0, 0.6, i) for i=0:4]...]
+
+show_graph(graph; locs=locations)
+
+# Then we define the matching problem as
+problem = Matching(graph);
+
+# ## Solving properties
diff --git a/examples/MaxCut/main.jl b/examples/MaxCut/main.jl
@@ -1,7 +1,54 @@
-# # Max-Cut problem
+# # Cutting problem (Spin-glass problem)
 
-# ## Problem definition
+# !!! note
+#     This tutorial only covers the cutting problem specific features,
+#     It is recommended to read the [Independent set problem](@ref) tutorial too to know more about
+#     * how to optimize the tensor network contraction order,
+#     * what are the other graph properties computable,
+#     * how to select correct method to compute graph properties,
+#     * how to compute weighted graphs and handle open vertices.
 
+# ## Introduction
+using GraphTensorNetworks, Graphs
+
+# Please check the docstring of [`MaxCut`](@ref) for the definition of the vertex cutting problem.
+@doc MaxCut
+
+# In the following, we are going to defined an cutting problem for the Petersen graph.
+
+graph = Graphs.smallgraph(:petersen)
+
+# We can visualize this graph using the following function
+rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
+
+locations = [[rot15(0.0, 1.0, i) for i=0:4]..., [rot15(0.0, 0.6, i) for i=0:4]...]
+
+show_graph(graph; locs=locations)
+
+# Then we define the cutting problem as
+problem = MaxCut(graph);
 
 # ## Solving properties
-using GraphTensorNetworks, Graphs
+
+# ### Maximum cut size ``\gamma(G)``
+max_cut_size = solve(problem, SizeMax())[]
+
+# ### Counting properties
+# ##### graph polynomial
+# Since the variable ``x`` is defined on edges,
+# hence the coefficients of the polynomial is the number of configurations having different number of anti-parallel edges.
+max_config = solve(problem, GraphPolynomial())[]
+
+# ### Configuration properties
+# ##### finding one max cut solution
+max_edge_config = solve(problem, SingleConfigMax())[]
+
+# These configurations are defined on edges, we need to find a valid assignment on vertices
+max_vertex_config = cut_assign(graph, max_edge_config.c.data)
+
+max_cut_size_verify = cut_size(graph, max_vertex_config)
+
+# You should see a consistent result as above `max_cut_size`.
+
+show_graph(graph; locs=locations, colors=[
+        iszero(max_vertex_config[i]) ? "white" : "red" for i=1:nv(graph)])
diff --git a/examples/MaxinalIndependence/main.jl b/examples/MaxinalIndependence/main.jl
@@ -0,0 +1,50 @@
+# # Maximal independent set problem
+
+# !!! note
+#     This tutorial only covers the maximal independent set problem specific features,
+#     It is recommended to read the [Independent set problem](@ref) tutorial too to know more about
+#     * how to optimize the tensor network contraction order,
+#     * what are the other graph properties computable,
+#     * how to select correct method to compute graph properties,
+#     * how to compute weighted graphs and handle open vertices.
+
+# ## Introduction
+using GraphTensorNetworks, Graphs
+
+# Please check the docstring of [`MaximalIndependence`](@ref) for the definition of the maximal independence problem.
+@doc MaximalIndependence
+
+# In the following, we are going to defined an cutting problem for the Petersen graph.
+
+graph = Graphs.smallgraph(:petersen)
+
+# We can visualize this graph using the following function
+rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
+
+locations = [[rot15(0.0, 1.0, i) for i=0:4]..., [rot15(0.0, 0.6, i) for i=0:4]...]
+
+show_graph(graph; locs=locations)
+
+# Then we define the maximal independent set problem as
+problem = MaximalIndependence(graph);
+
+# The tensor network structure is different from that of [`Independence`](@ref),
+# Its tensor is defined on a vertex and its neighbourhood,
+# and it makes the contraction of [`MaximalIndependence`](@ref) much harder.
+
+# ## Solving properties
+
+# ### Counting properties
+# ##### maximal independence polynomial
+max_config = solve(problem, GraphPolynomial())[]
+
+# Since it only counts the maximal independent sets, the first several coefficients are 0.
+
+# ### Configuration properties
+# ##### finding all maximal independent set
+max_edge_config = solve(problem, ConfigsAll())[]
+
+# This result should be consistent with that given by the [Bron Kerbosch algorithm](https://en.wikipedia.org/wiki/Bron%E2%80%93Kerbosch_algorithm) on the complement of Petersen graph.
+maximal_cliques = maximal_cliques(complement(graph))
+
+# For sparse graphs, the generic tensor network approach is usually much faster and memory efficient.
diff --git a/examples/Others/main.jl b/examples/Others/main.jl
@@ -7,7 +7,4 @@
 # A vertex cover is a complement of an independent set.
 
 # ### Maximal clique problem
-# The maximal clique of graph ``G`` is a maximal clique of ``G``'s complement graph.
-
-# ### Spin-glass problem
-# It is another way of saying the Max-Cut problem.
+# The maximal clique of graph ``G`` is a maximal clique of ``G``'s complement graph.
diff --git a/examples/PaintShop/main.jl b/examples/PaintShop/main.jl
@@ -0,0 +1,19 @@
+# # Binary paint shop problem
+
+# !!! note
+#     This tutorial only covers the binary paint shop problem specific features,
+#     It is recommended to read the [Independent set problem](@ref) tutorial too to know more about
+#     * how to optimize the tensor network contraction order,
+#     * what are the other graph properties computable,
+#     * how to select correct method to compute graph properties,
+#     * how to compute weighted graphs and handle open vertices.
+
+# ## Introduction
+using GraphTensorNetworks, Graphs
+
+# Please check the docstring of [`PaintShop`](@ref) for the definition of the binary paint shop problem.
+@doc PaintShop
+
+# In the following, we are going to defined a binary paint shop problem for the following string
+
+sequence = "abaccb"
diff --git a/notebooks/tropicalwidget_simple.jl b/notebooks/tropicalwidget_simple.jl
@@ -44,7 +44,7 @@ locs = let
 end
 
 # ╔═╡ 9d0ba8b7-5a34-418d-97cf-863f376f8453
-graph = unitdisk_graph(locs, 0.23) # SimpleGraph
+graph = unit_disk_graph(locs, 0.23) # SimpleGraph
 
 # ╔═╡ 78ee8772-83e4-40fb-8151-0123370481d9
 vizconfig(graph; locs=locs, config=rand(Bool, 12), graphsize=8cm)

diff --git a/notebooks/tutorial.jl b/notebooks/tutorial.jl
@@ -18,7 +18,7 @@ md"## High level interfaces"
 locs = [rand(2) for i=1:70];
 
 # ╔═╡ 1522938d-d60b-4133-8625-5a09615a7b26
-g = unitdisk_graph(locs, 0.2)
+g = unit_disk_graph(locs, 0.2)
 
 # ╔═╡ 05ac5610-4a1d-4433-ba0d-1d13fd8a6733
 vizconfig(g; locs=locs, unit=0.5)