[WIP] fix config feature add (#40)

* fix config feature add

* vertices fixed feature for all graph problem

Author: Fanerst

docs/src/ref.md

@@ -28,6 +28,7 @@ terms
 flavors
 get_weights
 nflavor
+fixedvertices
 ```
 
 #### Graph Problem Utilities

src/GenericTensorNetworks.jl

@@ -30,7 +30,7 @@ export line_graph
 
 # Tensor Networks (Graph problems)
 export GraphProblem, optimize_code, NoWeight
-export flavors, labels, terms, nflavor, get_weights
+export flavors, labels, terms, nflavor, get_weights, fixedvertices
 export IndependentSet, mis_compactify!, is_independent_set
 export MaximalIS, is_maximal_independent_set
 export MaxCut, cut_size

src/networks/Coloring.jl

@@ -10,25 +10,29 @@ struct Coloring{K,CT<:AbstractEinsum,WT<:Union{NoWeight, Vector}} <: GraphProble
     code::CT
     graph::SimpleGraph{Int}
     weights::WT
+    fixedvertices::Dict{Int,Int}
 end
-Coloring{K}(code::ET, graph::SimpleGraph, weights::Union{NoWeight, Vector}) where {K,ET<:AbstractEinsum} = Coloring{K,ET,typeof(weights)}(code, graph, weights)
+Coloring{K}(code::ET, graph::SimpleGraph, weights::Union{NoWeight, Vector}, fixedvertices::Dict{Int,Int}) where {K,ET<:AbstractEinsum} = Coloring{K,ET,typeof(weights)}(code, graph, weights, fixedvertices)
 # same network layout as independent set.
-function Coloring{K}(g::SimpleGraph; weights=NoWeight(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing) where K
+function Coloring{K}(g::SimpleGraph; weights=NoWeight(), openvertices=(), fixedvertices=Dict{Int,Int}(), optimizer=GreedyMethod(), simplifier=nothing) where K
     @assert weights isa NoWeight || length(weights) == ne(g)
     rawcode = EinCode(([[i] for i in Graphs.vertices(g)]..., # labels for vertex tensors
                        [[minmax(e.src,e.dst)...] for e in Graphs.edges(g)]...), collect(Int, openvertices))  # labels for edge tensors
-    code = _optimize_code(rawcode, uniformsize(rawcode, 2), optimizer, simplifier)
-    Coloring{K}(code, g, weights)
+    code = _optimize_code(rawcode, uniformsize_fix(rawcode, 2, fixedvertices), optimizer, simplifier)
+    Coloring{K}(code, g, weights, fixedvertices)
 end
 
 flavors(::Type{<:Coloring{K}}) where K = collect(0:K-1)
 get_weights(c::Coloring{K}, i::Int) where K = fill(c.weights[i], K)
 terms(gp::Coloring) = getixsv(gp.code)[1:nv(gp.graph)]
 labels(gp::Coloring) = [1:nv(gp.graph)...]
+fixedvertices(gp::Coloring) = gp.fixedvertices
 
 function generate_tensors(x::T, c::Coloring{K}) where {K,T}
     ixs = getixsv(c.code)
-    return vcat(add_labels!([coloringv(T, K) for i=1:nv(c.graph)], ixs[1:nv(c.graph)], labels(c)), [coloringb(x, K) .^ get_weights(c, i) for i=1:ne(c.graph)])
+    return select_dims(vcat(
+        add_labels!([coloringv(T, K) for i=1:nv(c.graph)], ixs[1:nv(c.graph)], labels(c)), [coloringb(x, K) .^ get_weights(c, i) for i=1:ne(c.graph)]
+    ), ixs, fixedvertices(c))
 end
 
 # coloring bond tensor

src/networks/DominatingSet.jl

@@ -11,25 +11,27 @@ struct DominatingSet{CT<:AbstractEinsum,WT<:Union{NoWeight, Vector}} <: GraphPro
     code::CT
     graph::SimpleGraph{Int}
     weights::WT
+    fixedvertices::Dict{Int,Int}
 end
 
-function DominatingSet(g::SimpleGraph; weights=NoWeight(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing)
+function DominatingSet(g::SimpleGraph; weights=NoWeight(), openvertices=(), fixedvertices=Dict{Int,Int}(), optimizer=GreedyMethod(), simplifier=nothing)
     @assert weights isa NoWeight || length(weights) == nv(g)
     rawcode = EinCode(([[Graphs.neighbors(g, v)..., v] for v in Graphs.vertices(g)]...,), collect(Int, openvertices))
-    DominatingSet(_optimize_code(rawcode, uniformsize(rawcode, 2), optimizer, simplifier), g, weights)
+    DominatingSet(_optimize_code(rawcode, uniformsize_fix(rawcode, 2, fixedvertices), optimizer, simplifier), g, weights, fixedvertices)
 end
 
 flavors(::Type{<:DominatingSet}) = [0, 1]
 get_weights(gp::DominatingSet, i::Int) = [0, gp.weights[i]]
 terms(gp::DominatingSet) = getixsv(gp.code)
 labels(gp::DominatingSet) = [1:length(getixsv(gp.code))...]
+fixedvertices(gp::DominatingSet) = gp.fixedvertices
 
 function generate_tensors(x::T, mi::DominatingSet) where T
     ixs = getixsv(mi.code)
     isempty(ixs) && return []
-	return add_labels!(map(enumerate(ixs)) do (i, ix)
+	return select_dims(add_labels!(map(enumerate(ixs)) do (i, ix)
         dominating_set_tensor((Ref(x) .^ get_weights(mi, i))..., length(ix))
-    end, ixs, labels(mi))
+    end, ixs, labels(mi)), ixs, fixedvertices(mi))
 end
 function dominating_set_tensor(a::T, b::T, d::Int) where T
     t = zeros(T, fill(2, d)...)

src/networks/IndependentSet.jl

@@ -12,29 +12,31 @@ struct IndependentSet{CT<:AbstractEinsum,WT<:Union{NoWeight, Vector}} <: GraphPr
     code::CT
     graph::SimpleGraph{Int}
     weights::WT
+    fixedvertices::Dict{Int,Int}
 end
 
-function IndependentSet(g::SimpleGraph; weights=NoWeight(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing)
+function IndependentSet(g::SimpleGraph; weights=NoWeight(), openvertices=(), fixedvertices=Dict{Int,Int}(), optimizer=GreedyMethod(), simplifier=nothing)
     @assert weights isa NoWeight || length(weights) == nv(g)
     rawcode = EinCode([[[i] for i in Graphs.vertices(g)]..., # labels for vertex tensors
                        [[minmax(e.src,e.dst)...] for e in Graphs.edges(g)]...], collect(Int, openvertices))  # labels for edge tensors
-    code = _optimize_code(rawcode, uniformsize(rawcode, 2), optimizer, simplifier)
-    IndependentSet(code, g, weights)
+    code = _optimize_code(rawcode, uniformsize_fix(rawcode, 2, fixedvertices), optimizer, simplifier)
+    IndependentSet(code, g, weights, fixedvertices)
 end
 
 flavors(::Type{<:IndependentSet}) = [0, 1]
 get_weights(gp::IndependentSet, i::Int) = [0, gp.weights[i]]
 terms(gp::IndependentSet) = getixsv(gp.code)[1:nv(gp.graph)]
 labels(gp::IndependentSet) = [1:nv(gp.graph)...]
+fixedvertices(gp::IndependentSet) = gp.fixedvertices
 
 # generate tensors
 function generate_tensors(x::T, gp::IndependentSet) where T
     nv(gp.graph) == 0 && return []
     ixs = getixsv(gp.code)
     # we only add labels at vertex tensors
-    return vcat(add_labels!([misv(Ref(x) .^ get_weights(gp, i)) for i=1:nv(gp.graph)], ixs[1:nv(gp.graph)], labels(gp)),
+    return select_dims(vcat(add_labels!([misv(Ref(x) .^ get_weights(gp, i)) for i=1:nv(gp.graph)], ixs[1:nv(gp.graph)], labels(gp)),
             [misb(T, length(ix)) for ix in ixs[nv(gp.graph)+1:end]] # if n!=2, it corresponds to set packing problem.
-    )
+    ), ixs, fixedvertices(gp))
 end
 
 function misb(::Type{T}, n::Integer=2) where T

src/networks/Matching.jl

@@ -10,21 +10,23 @@ struct Matching{CT<:AbstractEinsum, WT<:Union{NoWeight,Vector}} <: GraphProblem
     code::CT
     graph::SimpleGraph{Int}
     weights::WT
+    fixedvertices::Dict{Int,Int}
 end
 
-function Matching(g::SimpleGraph; weights=NoWeight(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing)
+function Matching(g::SimpleGraph; weights=NoWeight(), openvertices=(),fixedvertices=Dict{Int,Int}(), optimizer=GreedyMethod(), simplifier=nothing)
     @assert weights isa NoWeight || length(weights) == ne(g)
     edges = [minmax(e.src,e.dst) for e in Graphs.edges(g)]
     rawcode = EinCode(vcat([[s] for s in edges], # labels for edge tensors
                 [[minmax(i,j) for j in neighbors(g, i)] for i in Graphs.vertices(g)]),
                 collect(Tuple{Int,Int}, openvertices))
-    Matching(_optimize_code(rawcode, uniformsize(rawcode, 2), optimizer, simplifier), g, weights)
+    Matching(_optimize_code(rawcode, uniformsize_fix(rawcode, 2, fixedvertices), optimizer, simplifier), g, weights, fixedvertices)
 end
 
 flavors(::Type{<:Matching}) = [0, 1]
 get_weights(m::Matching, i::Int) = [0, m.weights[i]]
 terms(gp::Matching) = getixsv(gp.code)[1:ne(gp.graph)]
 labels(gp::Matching) = getindex.(terms(gp))
+fixedvertices(gp::Matching) = gp.fixedvertices
 
 function generate_tensors(x::T, m::Matching) where T
     ne(m.graph) == 0 && return []
@@ -40,7 +42,7 @@ function generate_tensors(x::T, m::Matching) where T
         end
         push!(tensors, t)
     end
-    return add_labels!(tensors, ixs, labels(m))
+    return select_dims(add_labels!(tensors, ixs, labels(m)), ixs, fixedvertices(m))
 end
 
 function match_tensor(::Type{T}, n::Int) where T

src/networks/MaxCut.jl

@@ -11,24 +11,28 @@ struct MaxCut{CT<:AbstractEinsum,WT<:Union{NoWeight, Vector}} <: GraphProblem
     code::CT
     graph::SimpleGraph{Int}
     weights::WT
+    fixedvertices::Dict{Int,Int}
 end
-function MaxCut(g::SimpleGraph; weights=NoWeight(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing)
+function MaxCut(g::SimpleGraph; weights=NoWeight(), openvertices=(), fixedvertices=Dict{Int,Int}(), optimizer=GreedyMethod(), simplifier=nothing)
     @assert weights isa NoWeight || length(weights) == ne(g)
     rawcode = EinCode([[minmax(e.src,e.dst)...] for e in Graphs.edges(g)], collect(Int, openvertices))  # labels for edge tensors
-    MaxCut(_optimize_code(rawcode, uniformsize(rawcode, 2), optimizer, simplifier), g, weights)
+    MaxCut(_optimize_code(rawcode, uniformsize_fix(rawcode, 2, fixedvertices), optimizer, simplifier), g, weights, fixedvertices)
 end
 
 flavors(::Type{<:MaxCut}) = [0, 1]
 get_weights(gp::MaxCut, i::Int) = [0, gp.weights[i]]
 terms(gp::MaxCut) = getixsv(gp.code)
 labels(gp::MaxCut) = [1:nv(gp.graph)...]
+fixedvertices(gp::MaxCut) = gp.fixedvertices
 
 function generate_tensors(x::T, gp::MaxCut) where T
     ixs = getixsv(gp.code)
-    return add_labels!(map(enumerate(ixs)) do (i, ix)
+    tensors = map(enumerate(ixs)) do (i, ix)
         maxcutb((Ref(x) .^ get_weights(gp, i)) ...)
-    end, ixs, labels(gp))
+    end
+    return select_dims(add_labels!(tensors, ixs, labels(gp)), ixs, fixedvertices(gp))
 end
+
 function maxcutb(a, b)
     return [a b; b a]
 end

src/networks/MaximalIS.jl

@@ -10,25 +10,27 @@ struct MaximalIS{CT<:AbstractEinsum,WT<:Union{NoWeight, Vector}} <: GraphProblem
     code::CT
     graph::SimpleGraph
     weights::WT
+    fixedvertices::Dict{Int,Int}
 end
 
-function MaximalIS(g::SimpleGraph; weights=NoWeight(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing)
+function MaximalIS(g::SimpleGraph; weights=NoWeight(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing, fixedvertices=Dict{Int,Int}())
     @assert weights isa NoWeight || length(weights) == nv(g)
     rawcode = EinCode(([[Graphs.neighbors(g, v)..., v] for v in Graphs.vertices(g)]...,), collect(Int, openvertices))
-    MaximalIS(_optimize_code(rawcode, uniformsize(rawcode, 2), optimizer, simplifier), g, weights)
+    MaximalIS(_optimize_code(rawcode, uniformsize_fix(rawcode, 2, fixedvertices), optimizer, simplifier), g, weights, fixedvertices)
 end
 
 flavors(::Type{<:MaximalIS}) = [0, 1]
 get_weights(gp::MaximalIS, i::Int) = [0, gp.weights[i]]
 terms(gp::MaximalIS) = getixsv(gp.code)
 labels(gp::MaximalIS) = [1:length(getixsv(gp.code))...]
+fixedvertices(gp::MaximalIS) = gp.fixedvertices
 
 function generate_tensors(x::T, mi::MaximalIS) where T
     ixs = getixsv(mi.code)
     isempty(ixs) && return []
-	return add_labels!(map(enumerate(ixs)) do (i, ix)
+	return select_dims(add_labels!(map(enumerate(ixs)) do (i, ix)
         maximal_independent_set_tensor((Ref(x) .^ get_weights(mi, i))..., length(ix))
-    end, ixs, labels(mi))
+    end, ixs, labels(mi)), ixs, fixedvertices(mi))
 end
 function maximal_independent_set_tensor(a::T, b::T, d::Int) where T
     t = zeros(T, fill(2, d)...)

src/networks/PaintShop.jl

@@ -31,37 +31,39 @@ struct PaintShop{CT<:AbstractEinsum,LT} <: GraphProblem
     code::CT
     sequence::Vector{LT}
     isfirst::Vector{Bool}
+    fixedvertices::Dict{LT,Int}
 end
 
-function paintshop_from_pairs(pairs::AbstractVector{Tuple{Int,Int}}; openvertices=(), optimizer=GreedyMethod(), simplifier=nothing)
+function paintshop_from_pairs(pairs::AbstractVector{Tuple{Int,Int}}; openvertices=(), optimizer=GreedyMethod(), simplifier=nothing, fixedvertices=Dict{LT,Int}())
     n = length(pairs)
     @assert sort!(vcat(collect.(pairs)...)) == collect(1:2n)
     sequence = zeros(Int, 2*n)
     @inbounds for i=1:n
         sequence[pairs[i]] .= i
     end
-    return PaintShop(pairs; openvertices, optimizer, simplifier)
+    return PaintShop(pairs; openvertices, optimizer, simplifier, fixedvertices)
 end
-function PaintShop(sequence::AbstractVector{T}; openvertices=(), optimizer=GreedyMethod(), simplifier=nothing) where T
+function PaintShop(sequence::AbstractVector{T}; openvertices=(), fixedvertices=Dict{T,Int}(), optimizer=GreedyMethod(), simplifier=nothing) where T
     @assert all(l->count(==(l), sequence)==2, sequence)
     n = length(sequence)
     isfirst = [findfirst(==(sequence[i]), sequence) == i for i=1:n]
     rawcode = EinCode(vcat(
                 [[sequence[i], sequence[i+1]] for i=1:n-1], # labels for edge tensors
                 ),
                 collect(T, openvertices))
-    PaintShop(_optimize_code(rawcode, uniformsize(rawcode, 2), optimizer, simplifier), sequence, isfirst)
+    PaintShop(_optimize_code(rawcode, uniformsize_fix(rawcode, 2, fixedvertices), optimizer, simplifier), sequence, isfirst, fixedvertices)
 end
 
 flavors(::Type{<:PaintShop}) = [0, 1]
 get_weights(::PaintShop, i::Int) = [0, 1]
 terms(gp::PaintShop) = getixsv(gp.code)
 labels(gp::PaintShop) = unique(gp.sequence)
+fixedvertices(gp::PaintShop) = gp.fixedvertices
 
 function generate_tensors(x::T, c::PaintShop) where T
     ixs = getixsv(c.code)
     tensors = [paintshop_bond_tensor((Ref(x) .^ get_weights(c, i))...) for i=1:length(ixs)]
-    return add_labels!([flip_labels(tensors[i], c.isfirst[i], c.isfirst[i+1]) for i=1:length(ixs)], ixs, labels(c))
+    return select_dims(add_labels!([flip_labels(tensors[i], c.isfirst[i], c.isfirst[i+1]) for i=1:length(ixs)], ixs, labels(c)), ixs, fixedvertices(c))
 end
 
 function paintshop_bond_tensor(a::T, b::T) where T

src/networks/Satisfiability.jl

@@ -120,17 +120,19 @@ struct Satisfiability{CT<:AbstractEinsum,T,WT<:Union{NoWeight, Vector}} <: Graph
     code::CT
     cnf::CNF{T}
     weights::WT
+    fixedvertices::Dict{T,Int}
 end
 
-function Satisfiability(cnf::CNF{T}; weights=NoWeight(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing) where T
+function Satisfiability(cnf::CNF{T}; weights=NoWeight(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing, fixedvertices=Dict{T,Int}()) where T
     rawcode = EinCode([[getfield.(c.vars, :name)...] for c in cnf.clauses], collect(T, openvertices))
-    Satisfiability(_optimize_code(rawcode, uniformsize(rawcode, 2), optimizer, simplifier), cnf, weights)
+    Satisfiability(_optimize_code(rawcode, uniformsize_fix(rawcode, 2, fixedvertices), optimizer, simplifier), cnf, weights, fixedvertices)
 end
 
 flavors(::Type{<:Satisfiability}) = [0, 1]  # false, true
 get_weights(s::Satisfiability, i::Int) = [0, s.weights[i]]
 terms(gp::Satisfiability) = getixsv(gp.code)
 labels(gp::Satisfiability) = unique!(vcat(getixsv(gp.code)...))
+fixedvertices(gp::Satisfiability) = gp.fixedvertices
 
 """
     satisfiable(cnf::CNF, config::AbstractDict)
@@ -152,9 +154,14 @@ function generate_tensors(x::VT, sa::Satisfiability{CT,T}) where {CT,T,VT}
     cnf = sa.cnf
     ixs = getixsv(sa.code)
     isempty(cnf.clauses) && return []
-	return add_labels!(map(1:length(cnf.clauses)) do i
-        tensor_for_clause(cnf.clauses[i], (Ref(x) .^ get_weights(sa, i))...)
-    end, ixs, labels(sa))
+	return select_dims(
+        add_labels!(
+            map(1:length(cnf.clauses)) do i
+                tensor_for_clause(cnf.clauses[i], (Ref(x) .^ get_weights(sa, i))...)
+            end,
+            ixs, labels(sa))
+        , ixs, fixedvertices(sa)
+    )
 end
 
 function tensor_for_clause(c::CNFClause{T}, a, b) where T