First pass at a feasibility checker.

docs/src/manual/solutions.md

@@ -127,7 +127,7 @@ FEASIBLE_POINT::ResultStatusCode = 1
 ```
 Other common returns are `MOI.NO_SOLUTION`, and `MOI.INFEASIBILITY_CERTIFICATE`.
 The first means that the solver doesn't have a solution to return, and the
-second means that the primal solution is a certificate of dual infeasbility (a 
+second means that the primal solution is a certificate of dual infeasbility (a
 primal unbounded ray).
 
 You can also use [`has_values`](@ref), which returns `true` if there is a
@@ -200,7 +200,7 @@ FEASIBLE_POINT::ResultStatusCode = 1
 ```
 Other common returns are `MOI.NO_SOLUTION`, and `MOI.INFEASIBILITY_CERTIFICATE`.
 The first means that the solver doesn't have a solution to return, and the
-second means that the dual solution is a certificate of primal infeasbility (a 
+second means that the dual solution is a certificate of primal infeasbility (a
 dual unbounded ray).
 
 You can also use [`has_duals`](@ref), which returns `true` if there is a
@@ -258,7 +258,7 @@ And data, a 2-element Array{Float64,1}:
 
 ## Recommended workflow
 
-The recommended workflow for solving a model and querying the solution is 
+The recommended workflow for solving a model and querying the solution is
 something like the following:
 ```jldoctest solutions
 if termination_status(model) == MOI.OPTIMAL
@@ -427,7 +427,7 @@ For instance, this is how you can use this functionality:
 
 ```julia
 using JuMP
-model = Model() # You must use a solver that supports conflict refining/IIS 
+model = Model() # You must use a solver that supports conflict refining/IIS
 # computation, like CPLEX or Gurobi
 @variable(model, x >= 0)
 @constraint(model, c1, x >= 2)
@@ -489,3 +489,58 @@ for i in 2:result_count(model)
     end
 end
 ```
+
+## Checking feasibility of solutions
+
+To check the feasibility of a primal solution, use
+[`primal_feasibility_report`](@ref), which takes a `model`, a dictionary mapping
+each variable to a primal solution value (defaults to the last solved solution),
+and a tolerance `atol` (defaults to `0.0`).
+
+The function returns a dictionary which maps the infeasible constraint
+references to the distance between the primal value of the constraint and the
+nearest point in the corresponding set. A point is classed as infeasible if the
+distance is greater than the supplied tolerance `atol`.
+
+```jldoctest feasibility; filter=["x integer         => 0.1", "c1 : x + y ≤ 1.95 => 0.01"]
+julia> model = Model(GLPK.Optimizer);
+
+julia> @variable(model, x >= 1, Int);
+
+julia> @variable(model, y);
+
+julia> @constraint(model, c1, x + y <= 1.95);
+
+julia> point = Dict(x => 1.9, y => 0.06);
+
+julia> primal_feasibility_report(model, point)
+Dict{Any,Float64} with 2 entries:
+  c1 : x + y ≤ 1.95 => 0.01
+  x integer         => 0.1
+
+julia> primal_feasibility_report(model, point; atol = 0.02)
+Dict{Any,Float64} with 1 entry:
+  x integer => 0.1
+```
+
+If the point is feasible, an empty dictionary is returned:
+```jldoctest feasibility
+julia> primal_feasibility_report(model, Dict(x => 1.0, y => 0.0))
+Dict{Any,Float64} with 0 entries
+```
+
+To use the primal solution from a solve, omit the `point` argument:
+```jldoctest feasibility
+julia> optimize!(model)
+
+julia> primal_feasibility_report(model)
+Dict{Any,Float64} with 0 entries
+```
+
+Pass `skip_mising = true` to skip constraints which contain variables that are
+not in `point`:
+```jldoctest feasibility
+julia> primal_feasibility_report(model, Dict(x => 2.1); skip_missing = true)
+Dict{Any,Float64} with 1 entry:
+  x integer => 0.1
+```

docs/src/reference/solutions.md

@@ -72,3 +72,9 @@ SensitivityReport
 lp_objective_perturbation_range
 lp_rhs_perturbation_range
 ```
+
+## Feasibility
+
+```@docs
+primal_feasibility_report
+```

src/JuMP.jl

@@ -1281,6 +1281,7 @@ include("lp_sensitivity.jl")
 include("lp_sensitivity2.jl")
 include("callbacks.jl")
 include("file_formats.jl")
+include("feasibility_checker.jl")
 
 # JuMP exports everything except internal symbols, which are defined as those
 # whose name starts with an underscore. Macros whose names start with

src/feasibility_checker.jl

@@ -0,0 +1,210 @@
+#  Copyright 2017, Iain Dunning, Joey Huchette, Miles Lubin, and contributors
+#  This Source Code Form is subject to the terms of the Mozilla Public
+#  License, v. 2.0. If a copy of the MPL was not distributed with this
+#  file, You can obtain one at https://mozilla.org/MPL/2.0/.
+
+using LinearAlgebra
+
+function _last_primal_solution(model::Model)
+    if !has_values(model)
+        error(
+            "No primal solution is available. You must provide a point at " *
+            "which to check feasibility.",
+        )
+    end
+    return Dict(v => value(v) for v in all_variables(model))
+end
+
+"""
+    primal_feasibility_report(
+        model::Model,
+        point::AbstractDict{VariableRef,Float64} = _last_primal_solution(model),
+        atol::Float64 = 0.0,
+        skip_missing::Bool = false,
+    )::Dict{Any,Float64}
+
+Given a dictionary `point`, which maps variables to primal values, return a
+dictionary mapping the constraint reference of each constraint in `model` to the
+distance between the point and the nearest feasible point, if the distance is
+greater than `atol`.
+
+## Notes
+
+ * If `skip_missing = true`, constraints containing variables that are not in
+   `point` will be ignored.
+ * If `skip_missing = false` and a partial primal solution is provided, an error
+   will be thrown.
+ * If no point is provided, the primal solution from the last time the model was
+   solved is used.
+
+## Examples
+
+```jldoctest; setup=:(using JuMP)
+julia> model = Model();
+
+julia> @variable(model, 0.5 <= x <= 1);
+
+julia> primal_feasibility_report(model, Dict(x => 0.2))
+Dict{Any,Float64} with 1 entry:
+  x ≥ 0.5 => 0.3
+```
+"""
+function primal_feasibility_report(
+    model::Model,
+    point::AbstractDict{VariableRef,Float64} = _last_primal_solution(model);
+    atol::Float64 = 0.0,
+    skip_missing::Bool = false,
+)
+    function point_f(x::VariableRef)
+        fx = get(point, x, missing)
+        if ismissing(fx) && !skip_missing
+            error(
+                "point does not contain a value for variable $x. Provide a " *
+                "value, or pass `skip_missing = true`.",
+            )
+        end
+        return fx
+    end
+    violated_constraints = Dict{Any,Float64}()
+    for (F, S) in list_of_constraint_types(model)
+        _add_infeasible_constraints(
+            model,
+            F,
+            S,
+            violated_constraints,
+            point_f,
+            atol,
+        )
+    end
+    if num_nl_constraints(model) > 0
+        if skip_missing
+            error(
+                "`skip_missing = true` is not allowed when nonlinear " *
+                "constraints are present.",
+            )
+        end
+        _add_infeasible_nonlinear_constraints(
+            model,
+            violated_constraints,
+            point_f,
+            atol,
+        )
+    end
+    return violated_constraints
+end
+
+function _add_infeasible_constraints(
+    model::Model,
+    ::Type{F},
+    ::Type{S},
+    violated_constraints::Dict{Any,Float64},
+    point_f::Function,
+    atol::Float64,
+) where {F,S}
+    for con in all_constraints(model, F, S)
+        obj = constraint_object(con)
+        d = _distance_to_set(value.(obj.func, point_f), obj.set)
+        if d > atol
+            violated_constraints[con] = d
+        end
+    end
+    return
+end
+
+function _add_infeasible_nonlinear_constraints(
+    model::Model,
+    violated_constraints::Dict{Any,Float64},
+    point_f::Function,
+    atol::Float64,
+)
+    evaluator = NLPEvaluator(model)
+    MOI.initialize(evaluator, Symbol[])
+    g = zeros(num_nl_constraints(model))
+    MOI.eval_constraint(evaluator, g, point_f.(all_variables(model)))
+    for (i, con) in enumerate(model.nlp_data.nlconstr)
+        d = max(0.0, con.lb - g[i], g[i] - con.ub)
+        if d > atol
+            cref =
+                ConstraintRef(model, NonlinearConstraintIndex(i), ScalarShape())
+            violated_constraints[cref] = d
+        end
+    end
+    return
+end
+
+function _distance_to_set(::Any, set::MOI.AbstractSet)
+    return error(
+        "Feasibility checker for set type $(typeof(set)) has not been " *
+        "implemented yet.",
+    )
+end
+
+_distance_to_set(::Missing, ::MOI.AbstractSet) = 0.0
+
+###
+### MOI.AbstractScalarSets
+###
+
+function _distance_to_set(x::T, set::MOI.LessThan{T}) where {T<:Real}
+    return max(x - set.upper, zero(T))
+end
+
+function _distance_to_set(x::T, set::MOI.GreaterThan{T}) where {T<:Real}
+    return max(set.lower - x, zero(T))
+end
+
+function _distance_to_set(x::T, set::MOI.EqualTo{T}) where {T<:Number}
+    return abs(set.value - x)
+end
+
+function _distance_to_set(x::T, set::MOI.Interval{T}) where {T<:Real}
+    return max(x - set.upper, set.lower - x, zero(T))
+end
+
+function _distance_to_set(x::T, ::MOI.ZeroOne) where {T<:Real}
+    return min(abs(x - zero(T)), abs(x - one(T)))
+end
+
+function _distance_to_set(x::T, ::MOI.Integer) where {T<:Real}
+    return abs(x - round(x))
+end
+
+function _distance_to_set(x::T, set::MOI.Semicontinuous{T}) where {T<:Real}
+    return min(max(x - set.upper, set.lower - x, zero(T)), abs(x))
+end
+
+function _distance_to_set(x::T, set::MOI.Semiinteger{T}) where {T<:Real}
+    d = max(ceil(set.lower) - x, x - floor(set.upper), abs(x - round(x)))
+    return min(d, abs(x))
+end
+
+###
+### MOI.AbstractVectorSets
+###
+
+function _check_dimension(v::AbstractVector, s)
+    if length(v) != MOI.dimension(s)
+        throw(DimensionMismatch("Mismatch between value and set"))
+    end
+    return
+end
+
+function _distance_to_set(x::Vector{T}, set::MOI.Nonnegatives) where {T<:Real}
+    _check_dimension(x, set)
+    return LinearAlgebra.norm(max(-xi, zero(T)) for xi in x)
+end
+
+function _distance_to_set(x::Vector{T}, set::MOI.Nonpositives) where {T<:Real}
+    _check_dimension(x, set)
+    return LinearAlgebra.norm(max(xi, zero(T)) for xi in x)
+end
+
+function _distance_to_set(x::Vector{T}, set::MOI.Zeros) where {T<:Number}
+    _check_dimension(x, set)
+    return LinearAlgebra.norm(x)
+end
+
+function _distance_to_set(x::Vector{T}, set::MOI.Reals) where {T<:Real}
+    _check_dimension(x, set)
+    return zero(T)
+end