Refactor problem size interface (#91)

* update

* tests pass

* fix deps

Author: GiggleLiu

Project.toml

@@ -11,8 +11,6 @@ Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078"
 PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 UnitDiskMapping = "1b61a8d9-79ed-4491-8266-ef37f39e1727"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [compat]
 BitBasis = "0.9"

docs/src/rules.md

@@ -8,4 +8,4 @@ A problem reduction rule is a function that reduces a problem to another problem
 - [`extract_solution`](@ref): Extract the solution of the target problem to the original problem.
 
 Optional functions include:
-- [`reduction_complexity`](@ref): The computational complexity of the reduction rule.
+- [`reduce_size`](@ref): Infer the size of the target problem from the source problem size.

src/ProblemReductions.jl

@@ -5,7 +5,6 @@ using DocStringExtensions
 using PrettyTables
 using BitBasis
 using MLStyle
-using Random
 
 export @bit_str
 export TruthTable
@@ -32,7 +31,7 @@ export MaximalIS
 export PaintShop
 
 # rules
-export target_problem, AbstractProblem, AbstractReductionResult, reduceto, extract_solution, extract_multiple_solutions, reduction_complexity
+export target_problem, AbstractProblem, AbstractReductionResult, reduceto, extract_solution, extract_multiple_solutions, reduce_size
 export LogicGadget, truth_table
 export ReductionSATTo3SAT
 export ReductionCircuitToSpinGlass, ReductionMaxCutToSpinGlass, ReductionSpinGlassToMaxCut, ReductionVertexCoveringToSetCovering, ReductionSatToColoring,

src/models/Circuit.jl

@@ -218,6 +218,7 @@ Base.show(io::IO, ::MIME"text/plain", x::CircuitSAT) = show(io, x)
 # variables interface
 variables(c::CircuitSAT) = collect(1:length(c.symbols))
 flavors(::Type{<:CircuitSAT}) = [0, 1]
+problem_size(c::CircuitSAT) = (; num_exprs=length(c.circuit.exprs), num_variables=length(c.symbols))
 
 # parameters interface
 parameters(sat::CircuitSAT) = Int[]

src/models/Coloring.jl

@@ -18,7 +18,7 @@ struct Coloring{K, WT<:AbstractVector} <:AbstractProblem
     end
 end
 Base.:(==)(a::Coloring, b::Coloring) = a.graph == b.graph && a.weights == b.weights
-problem_size(c::Coloring) = nv(c.graph)^2
+problem_size(c::Coloring) = (; num_vertices=nv(c.graph), num_edges=ne(c.graph))
 
 # variables interface
 variables(gp::Coloring{K}) where K = collect(1:nv(gp.graph))

src/models/DominatingSet.jl

@@ -21,6 +21,7 @@ Base.:(==)(a::DominatingSet, b::DominatingSet) = ( a.graph == b.graph )
 # Variables Interface
 variables(gp::DominatingSet) = [1:nv(gp.graph)...]
 flavors(::Type{<:DominatingSet}) = [0, 1]
+problem_size(c::DominatingSet) = (; num_vertices=nv(c.graph), num_edges=ne(c.graph))
 
 """
     evaluate(c::DominatingSet, config)

src/models/Factoring.jl

@@ -17,6 +17,7 @@ end
 # variables interface
 variables(f::Factoring) = collect(1:f.m+f.n)
 flavors(::Type{Factoring}) = [0, 1]
+problem_size(f::Factoring) = (; num_bits_first=f.m, num_bits_second=f.n)
 
 # utilities
 function evaluate(f::Factoring, config)

src/models/IndependentSet.jl

@@ -22,6 +22,7 @@ Base.:(==)(a::IndependentSet, b::IndependentSet) = ( a.graph == b.graph )
 # Variables Interface
 variables(gp::IndependentSet) = [1:nv(gp.graph)...]
 flavors(::Type{<:IndependentSet}) = [0, 1]
+problem_size(c::IndependentSet) = (; num_vertices=nv(c.graph), num_edges=ne(c.graph))
 
 """
     evaluate(c::IndependentSet, config)

src/models/Matching.jl

@@ -21,6 +21,7 @@ Base.:(==)(a::Matching, b::Matching) = a.graph == b.graph && a.weights == b.weig
 flavors(::Type{<:Matching}) = [0, 1]
 variables(gp::Matching) = collect(1:ne(gp.graph))
 num_variables(gp::Matching) = ne(gp.graph)
+problem_size(c::Matching) = (; num_vertices=nv(c.graph), num_edges=ne(c.graph))
 
 # weights interface
 parameters(c::Matching) = c.weights

src/models/MaxCut.jl

@@ -24,6 +24,7 @@ Base.:(==)(a::MaxCut, b::MaxCut) = a.graph == b.graph && a.weights == b.weights
 variables(gp::MaxCut) = [1:nv(gp.graph)...]
 num_variables(gp::MaxCut) = nv(gp.graph)
 flavors(::Type{<:MaxCut}) = [0, 1] #choose it or not
+problem_size(c::MaxCut) = (; num_vertices=nv(c.graph), num_edges=ne(c.graph))
 
 # weights interface
 parameters(c::MaxCut) = [[c.weights[i] for i=1:ne(c.graph)]...]