diff --git a/.travis.yml b/.travis.yml
index e5ad5c9..d0fa50d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -3,13 +3,8 @@ os:
   - linux
 julia:
   - 1.0
-  - 1.3
-  - nightly
+  - 1.4
 codecov: true
 notifications:
   email: false
 
-jobs:
-  allow_failures:
-  - julia: nightly
-
diff --git a/Project.toml b/Project.toml
index ea31e08..adf7036 100644
--- a/Project.toml
+++ b/Project.toml
@@ -9,7 +9,7 @@ MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 
 [compat]
-MathOptInterface = "~0.9.12"
+MathOptInterface = "0.9.12"
 DataStructures = "0.17.10"
 julia = "1"
 
diff --git a/README.md b/README.md
index 2b7bb5b..3d8c250 100644
--- a/README.md
+++ b/README.md
@@ -150,6 +150,12 @@ MOI.set(model, QuadraticToBinary.VariablePrecision(), vi, val)
 The precision for each varible will be `val * (UB - LB)`. Where `UB` and `LB` are,
 respectively, the upper and lower bound of the variable.
 
+For the sake of simplicity, the following two attributes are made available:
+`QuadraticToBinary.FallbackUpperBound` and `QuadraticToBinary.FallbackLowerBound`.
+As usual, these can be get and set with the `MOI.get` and `MOI.set` methods.
+These allow setting bounds used in variables that have no explicit upper bounds
+and need to be expanded.
+
 
 ## Reference
 
diff --git a/src/moi.jl b/src/moi.jl
index 0d2cbcb..6f02c9c 100644
--- a/src/moi.jl
+++ b/src/moi.jl
@@ -10,43 +10,6 @@ const EQ{T} = MOI.EqualTo{T}
 const LT{T} = MOI.LessThan{T}
 const GT{T} = MOI.GreaterThan{T}
 
-# same as MOI except for quad stuff
-MOIU.@model(NonQuadraticModel,
-       (MOI.ZeroOne, MOI.Integer),
-       (MOI.EqualTo, MOI.GreaterThan, MOI.LessThan, MOI.Interval,
-        MOI.Semicontinuous, MOI.Semiinteger),
-       (MOI.Reals, MOI.Zeros, MOI.Nonnegatives, MOI.Nonpositives,
-        MOI.Complements, MOI.NormInfinityCone, MOI.NormOneCone,
-        MOI.SecondOrderCone, MOI.RotatedSecondOrderCone,
-        MOI.GeometricMeanCone, MOI.ExponentialCone, MOI.DualExponentialCone,
-        MOI.RelativeEntropyCone, MOI.NormSpectralCone, MOI.NormNuclearCone,
-        MOI.PositiveSemidefiniteConeTriangle, MOI.PositiveSemidefiniteConeSquare,
-        MOI.RootDetConeTriangle, MOI.RootDetConeSquare, MOI.LogDetConeTriangle,
-        MOI.LogDetConeSquare),
-       (MOI.PowerCone, MOI.DualPowerCone, MOI.SOS1, MOI.SOS2),
-       (),
-       (MOI.ScalarAffineFunction,),
-       (MOI.VectorOfVariables,),
-       (MOI.VectorAffineFunction,))
-
-MOIU.@model(PureQuadraticModel,
-       (MOI.ZeroOne, MOI.Integer),
-       (MOI.EqualTo, MOI.GreaterThan, MOI.LessThan, MOI.Interval,
-        MOI.Semicontinuous, MOI.Semiinteger),
-       (MOI.Reals, MOI.Zeros, MOI.Nonnegatives, MOI.Nonpositives,
-        MOI.Complements, MOI.NormInfinityCone, MOI.NormOneCone,
-        MOI.SecondOrderCone, MOI.RotatedSecondOrderCone,
-        MOI.GeometricMeanCone, MOI.ExponentialCone, MOI.DualExponentialCone,
-        MOI.RelativeEntropyCone, MOI.NormSpectralCone, MOI.NormNuclearCone,
-        MOI.PositiveSemidefiniteConeTriangle, MOI.PositiveSemidefiniteConeSquare,
-        MOI.RootDetConeTriangle, MOI.RootDetConeSquare, MOI.LogDetConeTriangle,
-        MOI.LogDetConeSquare),
-       (MOI.PowerCone, MOI.DualPowerCone, MOI.SOS1, MOI.SOS2,),
-       (),
-       (MOI.ScalarQuadraticFunction,),
-       (),
-       (MOI.VectorQuadraticFunction,))
-
 const SCALAR_SETS = Union{
     MOI.GreaterThan{Float64},
     MOI.LessThan{Float64},
@@ -164,11 +127,15 @@ mutable struct Optimizer{T, OT <: MOI.ModelLike} <: MOI.AbstractOptimizer
 
     has_quad_change::Bool
 
-    function Optimizer{T}(optimizer::OT) where {T, OT <: MOI.ModelLike}
+    fallback_lb::Float64
+    fallback_ub::Float64
+
+    function Optimizer{T}(optimizer::OT; lb = -Inf, ub = +Inf, global_precision = 1e-4
+        ) where {T, OT <: MOI.ModelLike}
         # TODO optimizer must support binary, and affine in less and greater
         return new{T, OT}(
             optimizer,
-            1e-4,
+            global_precision,
             nothing,
             Dict{CI, MOI.ScalarQuadraticFunction{T}}(),
             Dict{CI, MOI.VectorQuadraticFunction{T}}(),
@@ -176,7 +143,9 @@ mutable struct Optimizer{T, OT <: MOI.ModelLike} <: MOI.AbstractOptimizer
             Dict{CI, VI}(),
             Dict{Tuple{VI,VI}, VI}(),
             nothing,
-            false
+            false,
+            lb,
+            ub,
             )
     end
 end
@@ -522,16 +491,38 @@ function MOI.set(model::Optimizer, attr::MOI.ConstraintFunction,
     error("Operation not allowed. Quadratic functions cant be modified.")
 end
 
+function MOI.add_constrained_variable(model::Optimizer, s::S
+) where {S<:MOI.AbstractScalarSet}
+    v, c = MOI.add_constrained_variable(model.optimizer, s)
+    model.original_variables[v] = VariableInfo()
+    return v, c
+end
+function MOI.add_constrained_variables(model::Optimizer, s::Vector{S}
+) where {S<:MOI.AbstractScalarSet}
+    vs, cs = MOI.add_constrained_variables(model.optimizer, s)
+    for v in vs
+        model.original_variables[v] = VariableInfo()
+    end
+    return vs, cs
+end
+function MOI.add_constrained_variables(model::Optimizer, s::S
+) where {S<:MOI.AbstractVectorSet}
+    vs, cs = MOI.add_constrained_variables(model.optimizer, s)
+    for v in vs
+        model.original_variables[v] = VariableInfo()
+    end
+    return vs, cs
+end
+
 function MOI.add_constraint(model::Optimizer, f::F, s::S
 ) where {F<:MOI.AbstractFunction, S<:MOI.AbstractSet}
-    ci = MOI.add_constraint(model.optimizer, f, s)
-    return ci
+    return MOI.add_constraint(model.optimizer, f, s)
 end
 function MOI.add_constraints(model::Optimizer, f::Vector{F}, s::Vector{S}
 ) where {F, S}
-    cis = MOI.add_constraints(model.optimizer, f, s)
-    return cis
+    return MOI.add_constraints(model.optimizer, f, s)
 end
+
 function MOI.add_constraint(model::Optimizer, f::F, s::S
 ) where {F<:QuadraticFunction{T}, S<:MOI.AbstractSet} where T
     fc = MOIU.canonical(f)
@@ -718,6 +709,35 @@ function cache_bounds(
     return
 end
 
+function MOI.add_constrained_variables(
+    model::Optimizer, s::Vector{S}
+) where {S <: SCALAR_SETS}
+    vs, c = MOI.add_constrained_variables(model.optimizer, s)
+    # from add var
+    for v in vs
+        model.original_variables[v] = VariableInfo()
+    end
+    # from add con
+    f = MOI.SingleVariable.(vs)
+    for i in eachindex(f)
+        cache_bounds(model, f[i], s[i])
+        model.ci_to_var[c[i]] = f[i].variable
+    end
+    return vs, c
+end
+function MOI.add_constrained_variable(
+    model::Optimizer, s::S
+) where {S <: SCALAR_SETS}
+    v, ci = MOI.add_constrained_variable(model.optimizer, s)
+    # from add var
+    model.original_variables[v] = VariableInfo()
+    # from add con
+    f = MOI.SingleVariable(v)
+    cache_bounds(model, f, s)
+    model.ci_to_var[ci] = f.variable
+    return v, ci
+end
+
 function MOI.add_constraints(
     model::Optimizer, f::Vector{MOI.SingleVariable}, s::Vector{S}
 ) where {S <: SCALAR_SETS}
@@ -822,6 +842,40 @@ end
 
 scalar_type(S::Type{MOI.ZeroOne}) = BINARY
 scalar_type(S::Type{MOI.Integer}) = INTEGER
+
+function MOI.add_constrained_variables(
+    model::Optimizer, s::Vector{S}
+) where {S <: SCALAR_TYPES}
+    vs, c = MOI.add_constrained_variables(model.optimizer, s)
+    # from add var
+    for v in vs
+        model.original_variables[v] = VariableInfo()
+    end
+    # from add con
+    f = MOI.SingleVariable.(vs)
+    for i in eachindex(f)
+        info = model.original_variables[f[i].variable]
+        info.type = scalar_type(S)
+    end
+    for i in eachindex(c)
+        model.ci_to_var[c[i]] = f[i].variable
+    end
+    return vs, c
+end
+function MOI.add_constrained_variable(
+    model::Optimizer, s::S
+) where {S <: SCALAR_TYPES}
+    v, ci = MOI.add_constrained_variable(model.optimizer, s)
+    # from add var
+    model.original_variables[v] = VariableInfo()
+    # from add con
+    f = MOI.SingleVariable(v)
+    info = model.original_variables[f.variable]
+    info.type = scalar_type(S)
+    model.ci_to_var[ci] = f.variable
+    return v, ci
+end
+
 function MOI.add_constraints(
     model::Optimizer, f::Vector{MOI.SingleVariable}, s::Vector{S}
 ) where {S <: SCALAR_TYPES}
@@ -929,9 +983,22 @@ function delete_additional_constraints(model)
     end
 end
 
-
-lower(info) = info.type == BINARY ? zero(typeof(info.lower)) : info.lower
-upper(info) = info.type == BINARY ? one(typeof(info.lower)) : info.upper
+function lower(model, info)
+    if info.type == BINARY 
+        return zero(typeof(info.lower))
+    elseif info.lower > -Inf
+        return info.lower
+    end
+    return model.fallback_lb
+end
+function upper(model, info)
+    if info.type == BINARY
+        return one(typeof(info.lower))
+    elseif info.upper < Inf
+        return info.upper
+    end
+    return model.fallback_ub
+end
 function get_precision(model::Optimizer{T}, x) where T
     info = model.original_variables[x]
     a = model.global_initial_precision
@@ -1006,10 +1073,10 @@ function build_approximation!(model::Optimizer)
     for pair in QT
         for x in pair
             info = model.original_variables[x]
-            if !(upper(info) < Inf)
+            if !(upper(model, info) < Inf)
                 error("Variable $x has no upper bound.")
             end
-            if !(lower(info) > -Inf)
+            if !(lower(model, info) > -Inf)
                 error("Variable $x has no lower bound.")
             end
         end
@@ -1054,8 +1121,8 @@ function build_approximation!(model::Optimizer)
     s28 = EQ{T}[]
     for xj in DS
         info = model.original_variables[xj]
-        Xu = upper(info)
-        Xl = lower(info)
+        Xu = upper(model, info)
+        Xl = lower(model, info)
         Δxj = Δx[xj]
         zj = z[xj]
         terms = [
@@ -1081,8 +1148,8 @@ function build_approximation!(model::Optimizer)
             xj = xa
         end
         info = model.original_variables[xj]
-        Xu = upper(info)
-        Xl = lower(info)
+        Xu = upper(model, info)
+        Xl = lower(model, info)
         #order is important in the pairs
         Δwij = Δw[(xa,xb)]
         wij = w[(xa,xb)]
@@ -1132,8 +1199,8 @@ function build_approximation!(model::Optimizer)
             xj = xa
         end
         info_i = model.original_variables[xi]
-        Xu_i = upper(info_i)
-        Xl_i = lower(info_i)
+        Xu_i = upper(model, info_i)
+        Xl_i = lower(model, info_i)
         Δwij = Δw[(xa,xb)]
         Δxj = Δx[xj]
         p = get_precision(model, xj)
@@ -1190,8 +1257,8 @@ function build_approximation!(model::Optimizer)
             xj = xa
         end
         info_i = model.original_variables[xi]
-        Xu_i = upper(info_i)
-        Xl_i = lower(info_i)
+        Xu_i = upper(model, info_i)
+        Xl_i = lower(model, info_i)
         zj = z[xj]
         xhij = xh[(xa,xb)]
         # 33
@@ -1362,6 +1429,8 @@ end
 
 struct VariablePrecision end
 struct GlobalVariablePrecision end
+struct FallbackUpperBound end
+struct FallbackLowerBound end
 
 function MOI.set(
     model::Optimizer,
@@ -1382,7 +1451,7 @@ end
 
 function MOI.set(
     model::Optimizer,
-    attr::GlobalVariablePrecision,
+    ::GlobalVariablePrecision,
     value::Union{Nothing, Float64}
 )
     val = value === nothing ? NaN : value
@@ -1391,18 +1460,41 @@ function MOI.set(
     return
 end
 function MOI.get(
-    model::Optimizer, attr::GlobalVariablePrecision, x::MOI.VariableIndex
+    model::Optimizer, ::GlobalVariablePrecision, x::MOI.VariableIndex
 )
     return model.global_initial_precision
 end
 
-function MOI.get(model::Optimizer, attr::MOI.NumberOfConstraints{F, S}) where {F, S}
+function MOI.set(model::Optimizer, ::FallbackLowerBound, val)
+    model.fallback_lb = val
+    return nothing
+end
+function MOI.set(model::Optimizer, ::FallbackLowerBound, ::Nothing)
+    model.fallback_lb = -Inf
+    return nothing
+end
+function MOI.get(model::Optimizer, ::FallbackLowerBound)
+    return model.fallback_lb
+end
+function MOI.set(model::Optimizer, ::FallbackUpperBound, val)
+    model.fallback_ub = val
+    return nothing
+end
+function MOI.set(model::Optimizer, ::FallbackUpperBound, ::Nothing)
+    model.fallback_ub = Inf
+    return nothing
+end
+function MOI.get(model::Optimizer, ::FallbackUpperBound)
+    return model.fallback_ub
+end
+
+function MOI.get(model::Optimizer, ::MOI.NumberOfConstraints{F, S}) where {F, S}
     # TODO: this could be more efficient.
     return length(MOI.get(model, MOI.ListOfConstraintIndices{F,S}()))
 end
 
 
-function MOI.get(model::Optimizer, ::MOI.ListOfConstraints)
+function MOI.get(::Optimizer, ::MOI.ListOfConstraints)
     constraints = Set{Tuple{DataType, DataType}}()
     error("TODO")
 end
diff --git a/test/moi.jl b/test/moi.jl
index a0ad9c0..8f83612 100644
--- a/test/moi.jl
+++ b/test/moi.jl
@@ -59,6 +59,74 @@ function ncqcp1test_mod(model::MOI.ModelLike, config::MOIT.TestConfig)
     end
 end
 
+function ncqcp1test_mod2(model::MOI.ModelLike, config::MOIT.TestConfig)
+    atol = config.atol
+    rtol = config.rtol
+    # Max 2x + y
+    # s.t. x * y <= 4 (c)
+    #      x, y >= 1
+
+    # @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
+    @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}())
+    @test MOI.supports_constraint(model, MOI.ScalarQuadraticFunction{Float64}, MOI.LessThan{Float64})
+
+    MOI.empty!(model)
+    @test MOI.is_empty(model)
+
+    x = MOI.add_variable(model)
+    y = MOI.add_variable(model)
+    @test MOI.get(model, MOI.NumberOfVariables()) == 2
+
+    # set upper fallback
+    MOI.set(model, QuadraticToBinary.FallbackUpperBound(), 10.0)
+    @test MOI.get(model, QuadraticToBinary.FallbackUpperBound()) == 10.0
+
+    # set and unset lower fallback
+    MOI.set(model, QuadraticToBinary.FallbackLowerBound(), 0.0)
+    @test MOI.get(model, QuadraticToBinary.FallbackLowerBound()) == 0.0
+    MOI.set(model, QuadraticToBinary.FallbackLowerBound(), nothing)
+    @test MOI.get(model, QuadraticToBinary.FallbackLowerBound()) == -Inf
+
+    vc1 = MOI.add_constraint(model, MOI.SingleVariable(x), MOI.GreaterThan(1.0))
+    @test vc1.value == x.value
+    vc2 = MOI.add_constraint(model, MOI.SingleVariable(y), MOI.GreaterThan(1.0))
+    @test vc2.value == y.value
+
+    cf = MOI.ScalarQuadraticFunction(
+        [MOI.ScalarAffineTerm(0.0, x)], [MOI.ScalarQuadraticTerm(1.0, x, y)], 0.0)
+    c = MOI.add_constraint(model, cf, MOI.LessThan(4.0))
+    @test MOI.get(model, MOI.NumberOfConstraints{
+        MOI.ScalarQuadraticFunction{Float64}, MOI.LessThan{Float64}}()) == 1
+
+    MOI.set(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),
+        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([2.0, 1.0], [x, y]), 0.0))
+    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
+    @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE
+
+    if config.query
+        @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c)
+    end
+
+    if config.solve
+        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+
+        MOI.optimize!(model)
+
+        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+
+        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+
+        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 9.0 atol=atol rtol=rtol
+
+        @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 4.0 atol=atol rtol=rtol
+        @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 1.0 atol=atol rtol=rtol
+
+        @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 4.0 atol=atol rtol=rtol
+        @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 4.0 atol=atol rtol=rtol
+        @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 1.0 atol=atol rtol=rtol
+    end
+end
+
 function ncqcp2test_mod(model::MOI.ModelLike, config::MOIT.TestConfig)
     atol = config.atol
     rtol = config.rtol
diff --git a/test/runtests.jl b/test/runtests.jl
index 7a03063..e1b7ee8 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -19,6 +19,7 @@ const CONFIG = MOIT.TestConfig(duals = false, infeas_certificates = false)
     for opt in optimizers
         MODEL = QuadraticToBinary.Optimizer{Float64}(opt)
         ncqcp1test_mod(MODEL, CONFIG_LOW_TOL)
+        ncqcp1test_mod2(MODEL, CONFIG_LOW_TOL)
         ncqcp2test_mod(MODEL, CONFIG_LOW_TOL)
     end
 end