docs and example

Author: c-allergic

docs/src/index.md

@@ -41,7 +41,7 @@ solve(
 Here the main function [`solve`](@ref) takes three input arguments, the problem instance of type [`IndependentSet`](@ref), the property instance of type [`GraphPolynomial`](@ref) and an optional key word argument `usecuda` to decide use GPU or not.
 If one wants to use GPU to accelerate the computation, the `, CUDA` should be uncommented.
 
-An [`IndependentSet`](@ref) instance takes two positional arguments to initialize, the graph instance that one wants to solve and the weights for each vertex. Here, we use a random regular graph with 20 vertices and degree 3, and the default uniform weight 1.
+An [`IndependentSet`](@ref) instance takes two positional arguments to initialize, the graph instance that one wants to solve and the get_weights for each vertex. Here, we use a random regular graph with 20 vertices and degree 3, and the default uniform weight 1.
 
 The [`GenericTensorNetwork`](@ref) function is a constructor for the problem instance, which takes the problem instance as the first argument and optional key word arguments. The key word argument `optimizer` is for specifying the tensor network optimization algorithm.
 The keyword argument `openvertices` is a tuple of labels for specifying the degrees of freedom not summed over, and `fixedvertices` is a label-value dictionary for specifying the fixed values of the degree of freedoms.

docs/src/ref.md

@@ -1,9 +1,9 @@
 # References
-## Graph problems
+## Constraint Satisfaction Problems
 ```@docs
 solve
 GenericTensorNetwork
-GraphProblem
+ConstraintSatisfactionProblem
 IndependentSet
 MaximalIS
 Matching
@@ -18,9 +18,9 @@ SetPacking
 OpenPitMining
 ```
 
-#### Graph Problem Interfaces
+#### Constraint Satisfaction Problem Interfaces
 
-To subtype [`GraphProblem`](@ref), a new type must contain a `code` field to represent the (optimized) tensor network.
+To subtype [`ConstraintSatisfactionProblem`](@ref), a new type must contain a `code` field to represent the (optimized) tensor network.
 Interfaces [`GenericTensorNetworks.generate_tensors`](@ref), [`labels`](@ref), [`flavors`](@ref) and [`get_weights`](@ref) are required.
 [`nflavor`](@ref) is optional.
 
@@ -30,12 +30,12 @@ labels
 energy_terms
 flavors
 get_weights
-chweights
+set_weights
 nflavor
 fixedvertices
 ```
 
-#### Graph Problem Utilities
+#### Constraint Satisfaction Problem Utilities
 ```@docs
 is_independent_set
 is_maximal_independent_set

examples/MaxCut.jl

@@ -7,7 +7,7 @@
 # In graph theory, a [cut](https://en.wikipedia.org/wiki/Cut_(graph_theory)) is a partition of the vertices of a graph into two disjoint subsets.
 # It is closely related to the [Spin-glass problem](@ref) in physics.
 # Finding the maximum cut is NP-Hard, where a maximum cut is a cut whose size is at least the size of any other cut,
-# where the size of a cut is the number of edges (or the sum of weights on edges) crossing the cut.
+# where the size of a cut is the number of edges (or the sum of get_weights on edges) crossing the cut.
 
 using GenericTensorNetworks, Graphs
 
@@ -40,7 +40,7 @@ problem = GenericTensorNetwork(maxcut)
 # where ``w_{ij}`` is a real number associated with edge ``(i, j)`` as the edge weight.
 # If and only if the bipartition cuts on edge ``(i, j)``,
 # this tensor contributes a factor ``x_{i}^{w_{ij}}`` or ``x_{j}^{w_{ij}}``.
-# Similarly, one can assign weights to vertices, which corresponds to the onsite energy terms in the spin glass.
+# Similarly, one can assign get_weights to vertices, which corresponds to the onsite energy terms in the spin glass.
 # The vertex tensor is
 # ```math
 # W(x_i, w_i) = \left(\begin{matrix}

examples/SpinGlass.jl

@@ -106,7 +106,7 @@ hyperedges = [[1,3,4,6,7], [4,7,8,12], [2,5,9,11,13],
     [1,2,14,15], [3,6,10,12,14], [8,14,15], 
     [1,2,6,11], [1,2,4,6,8,12]]
 
-weights = [-1, 1, -1, 1, -1, 1, -1, 1];
+get_weights = [-1, 1, -1, 1, -1, 1, -1, 1];
 
 # The energy function of the spin glass problem is
 # ```math
@@ -117,7 +117,7 @@ weights = [-1, 1, -1, 1, -1, 1, -1, 1];
 # \end{align*}
 # ```
 # A spin glass problem can be defined with the [`SpinGlass`](@ref) type as
-hyperspinglass = SpinGlass(num_vertices, hyperedges, weights)
+hyperspinglass = SpinGlass(num_vertices, hyperedges, get_weights)
 
 # The tensor network representation of the set packing problem can be obtained by
 hyperproblem = GenericTensorNetwork(hyperspinglass)

examples/weighted.jl

@@ -26,8 +26,8 @@ show_graph(graph, locations; format=:svg, vertex_colors=
 
 # The only solution space property that can not be defined for general real-weighted (not including integer-weighted) graphs is the [`GraphPolynomial`](@ref).
 
-# For the weighted MIS problem, a useful solution space property is the "energy spectrum", i.e. the largest several configurations and their weights.
-# We can use the solution space property is [`SizeMax`](@ref)`(10)` to compute the largest 10 weights.
+# For the weighted MIS problem, a useful solution space property is the "energy spectrum", i.e. the largest several configurations and their get_weights.
+# We can use the solution space property is [`SizeMax`](@ref)`(10)` to compute the largest 10 get_weights.
 spectrum = solve(problem, SizeMax(10))[]
 
 # The return value has type [`ExtendedTropical`](@ref), which contains one field `orders`.