Add docstrings to Model and Solution

README.md

@@ -125,7 +125,10 @@ MOI.set(model, PolyJuMP.MultiplierMaxdegree(), con, 2)
 The `SumOfSquares.Optimizer` of [SumOfSquares.jl](https://github.com/jump-dev/SumOfSquares.jl)
 is the analogous solver certifying the nonnegativity of the Lagrangian with
 the SOS cone instead; increasing the `PolyJuMP.MultiplierMaxdegree` attributes
-then gives the higher levels of the Lasserre hierarchy.
+then gives the higher levels of the Lasserre hierarchy and the candidate
+solutions are recovered from the atoms of the moment matrix given by the dual
+of the SOS constraint, using
+[MultivariateMoments](https://github.com/JuliaAlgebra/MultivariateMoments.jl).
 
 [CP16] Chandrasekaran, Venkat, and Parikshit Shah.
 *Relative entropy relaxations for signomial optimization.*

src/model.jl

@@ -1,3 +1,10 @@
+"""
+    mutable struct Model{T} <: MOI.ModelLike
+
+Model representing a polynomial optimization problem with objective
+`objective_sense` of the polynomial `objective_function` over the
+basic semialgebraic `set`.
+"""
 mutable struct Model{T} <: MOI.ModelLike
     variables::Dict{MOI.VariableIndex,VarType}
     objective_sense::MOI.OptimizationSense
@@ -17,6 +24,14 @@ function MP.variables(model::Model)
     return sort!(collect(values(model.variables)), rev = true)
 end
 
+"""
+    struct Solution{T}
+
+Candidate solution of `values` for the variables of a [`Model`](@ref) with
+corresponding `objective_value` and `max_constraint_violation`.
+The `status` classifies its feasibility given the tolerance that was used
+to compare the maximum constraint violation.
+"""
 struct Solution{T}
     values::Vector{T}
     objective_value::T