Cutting problem

Problem definition

In graph theory, a cut is a partition of the vertices of a graph into two disjoint subsets. It is closely related to the Spin-glass problem 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.

using GenericTensorNetworks, Graphs

In the following, we are going to defined an cutting problem for the Petersen graph.

graph = Graphs.smallgraph(:petersen)

{10, 15} undirected simple Int64 graph

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, 60.0, i) for i=0:4]..., [rot15(0.0, 30, i) for i=0:4]...]
show_graph(graph, locations; format=:svg)

Generic tensor network representation

We can define the cutting problem with the MaxCut type as

maxcut = MaxCut(graph)

MaxCut{UnitWeight, ZeroWeight}(Graphs.SimpleGraphs.SimpleGraph{Int64}(15, [[2, 5, 6], [1, 3, 7], [2, 4, 8], [3, 5, 9], [1, 4, 10], [1, 8, 9], [2, 9, 10], [3, 6, 10], [4, 6, 7], [5, 7, 8]]), UnitWeight(), ZeroWeight())

The tensor network representation of the cutting problem can be obtained by

problem = GenericTensorNetwork(maxcut)

GenericTensorNetwork{MaxCut{UnitWeight, ZeroWeight}, OMEinsum.DynamicNestedEinsum{Int64}, Int64}(MaxCut{UnitWeight, ZeroWeight}(Graphs.SimpleGraphs.SimpleGraph{Int64}(15, [[2, 5, 6], [1, 3, 7], [2, 4, 8], [3, 5, 9], [1, 4, 10], [1, 8, 9], [2, 9, 10], [3, 6, 10], [4, 6, 7], [5, 7, 8]]), UnitWeight(), ZeroWeight()), 5∘7∘10, 5∘7∘10 -> 
├─ 5∘6∘3∘7, 3∘6∘10 -> 5∘7∘10
│  ├─ 5∘6∘3∘7, 3∘5∘6∘7 -> 5∘6∘3∘7
│  │  ├─ 2∘5∘6, 3∘7∘2 -> 5∘6∘3∘7
│  │  │  ├─ 2∘5∘1, 1∘6 -> 2∘5∘6
│  │  │  │  ├─ 1∘2, 1∘5 -> 2∘5∘1
│  │  │  │  │  ⋮
│  │  │  │  │  
│  │  │  │  └─ 6, 1∘6 -> 1∘6
│  │  │  │     ⋮
│  │  │  │     
│  │  │  └─ 2∘3, 2∘7 -> 3∘7∘2
│  │  │     ├─ 3, 2∘3 -> 2∘3
│  │  │     │  ⋮
│  │  │     │  
│  │  │     └─ 7, 2∘7 -> 2∘7
│  │  │        ⋮
│  │  │        
│  │  └─ 3∘5∘9, 6∘7∘9 -> 3∘5∘6∘7
│  │     ├─ 3∘4, 5∘9∘4 -> 3∘5∘9
│  │     │  ├─ 4, 3∘4 -> 3∘4
│  │     │  │  ⋮
│  │     │  │  
│  │     │  └─ 4∘5, 4∘9 -> 5∘9∘4
│  │     │     ⋮
│  │     │     
│  │     └─ 6∘9, 7∘9 -> 6∘7∘9
│  │        ├─ 6∘9
│  │        └─ 7∘9
│  └─ 3∘6∘8, 8∘10 -> 3∘6∘10
│     ├─ 3∘8, 6∘8 -> 3∘6∘8
│     │  ├─ 8, 3∘8 -> 3∘8
│     │  │  ├─ 8
│     │  │  └─ 3∘8
│     │  └─ 6∘8
│     └─ 8∘10
└─ 5∘10, 7∘10 -> 5∘7∘10
   ├─ 10, 5∘10 -> 5∘10
   │  ├─ 10
   │  └─ 5∘10
   └─ 7∘10
, Dict{Int64, Int64}())

Theory (can skip)

We associated a vertex $v\in V$ with a boolean degree of freedom $s_v\in\{0, 1\}$. Then the maximum cutting problem can be encoded to tensor networks by mapping an edge $(i,j)\in E$ to an edge matrix labelled by $s_i$ and $s_j$

\[B(x_i, x_j, w_{ij}) = \left(\begin{matrix} 1 & x_{i}^{w_{ij}}\\ x_{j}^{w_{ij}} & 1 \end{matrix}\right),\]

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. The vertex tensor is

\[W(x_i, w_i) = \left(\begin{matrix} 1\\ x_{i}^{w_i} \end{matrix}\right),\]

where $w_i$ is a real number associated with vertex $i$ as the vertex weight.

Its contraction time space complexity is $2^{{\rm tw}(G)}$, where ${\rm tw(G)}$ is the tree-width of $G$.

Solving properties

Maximum cut size $\gamma(G)$

max_cut_size = solve(problem, SizeMax())[]

12.0ₜ

Counting properties

graph polynomial

The graph polynomial defined for the cutting problem is

\[C(G, x) = \sum_{k=0}^{\gamma(G)} c_k x^k,\]

where $\gamma(G)$ is the maximum cut size, $c_k/2$ is the number of cuts of size $k$ in graph $G=(V,E)$. Since the variable $x$ is defined on edges, 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_vertex_config = read_config(solve(problem, SingleConfigMax())[])

max_cut_size_verify = cut_size(graph, max_vertex_config)

0x000000000000000c

You should see a consistent result as above max_cut_size.

show_graph(graph, locations; vertex_colors=[
        iszero(max_vertex_config[i]) ? "white" : "red" for i=1:nv(graph)], format=:svg)

where red vertices and white vertices are separated by the cut.

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