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Make things work for general AbstractArrays #980

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merged 12 commits into from
Mar 1, 2017
Merged

Make things work for general AbstractArrays #980

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b87c015
Make things work for general AbstractArrays.
tkoolen Feb 24, 2017
6306e2c
Address comments.
tkoolen Feb 25, 2017
ae06063
Add test for non-Array AbstractArrays.
tkoolen Feb 25, 2017
722fd26
Widen Array/Scalar operations.
tkoolen Feb 25, 2017
553981a
Address comments.
tkoolen Feb 25, 2017
035cfe9
More tests for AbstractArray.
tkoolen Feb 25, 2017
5776b09
eachindex updates.
tkoolen Feb 25, 2017
5c300c4
Add tests for OffsetArrays. Fix a couple of issues related to non-one…
tkoolen Feb 28, 2017
a8e0ab9
Don't use splatting to convert from AbstractArray to Array.
tkoolen Feb 28, 2017
46b5548
Address splatting comments, fix addVectorizedConstraint for sparse ve…
tkoolen Feb 28, 2017
2254e20
Undo changes to addVectorizedConstraint, change constructconstraint i…
tkoolen Feb 28, 2017
bded621
Remove unnecessary type assert (copy paste mistake).
tkoolen Mar 1, 2017
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16 changes: 8 additions & 8 deletions src/JuMP.jl
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Expand Up @@ -233,7 +233,7 @@ end
setobjective(m::Model, something::Any) =
error("in setobjective: needs three arguments: model, objective sense (:Max or :Min), and expression.")

setobjective(::Model, ::Symbol, x::Array) =
setobjective(::Model, ::Symbol, x::AbstractArray) =
error("in setobjective: array of size $(size(x)) passed as objective; only scalar objectives are allowed")

function setsolver(m::Model, solver::MathProgBase.AbstractMathProgSolver)
Expand Down Expand Up @@ -477,7 +477,7 @@ Base.zero(::Variable) = zero(Variable)
Base.one(::Type{Variable}) = AffExpr(Variable[],Float64[],1.0)
Base.one(::Variable) = one(Variable)

function verify_ownership(m::Model, vec::Vector{Variable})
function verify_ownership(m::Model, vec::AbstractVector{Variable})
n = length(vec)
@inbounds for i in 1:n
vec[i].m !== m && return false
Expand All @@ -487,7 +487,7 @@ end

Base.copy(v::Variable, new_model::Model) = Variable(new_model, v.col)
Base.copy(x::Void, new_model::Model) = nothing
function Base.copy(v::Array{Variable}, new_model::Model)
function Base.copy(v::AbstractArray{Variable}, new_model::Model)
ret = similar(v, Variable, size(v))
for I in eachindex(v)
ret[I] = Variable(new_model, v[I].col)
Expand Down Expand Up @@ -529,7 +529,7 @@ type SDConstraint <: AbstractConstraint
end

# Special-case X ≥ 0, which is often convenient
function SDConstraint(lhs::Matrix, rhs::Number)
function SDConstraint(lhs::AbstractMatrix, rhs::Number)
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This change is not covered by tests.

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Fixed.

rhs == 0 || error("Cannot construct a semidefinite constraint with nonzero scalar bound $rhs")
SDConstraint(lhs)
end
Expand Down Expand Up @@ -741,12 +741,12 @@ function setRHS(c::LinConstrRef, rhs::Number)
end

Variable(m::Model,lower::Number,upper::Number,cat::Symbol,objcoef::Number,
constraints::JuMPArray,coefficients::Vector{Float64}, name::AbstractString="", value::Number=NaN) =
constraints::JuMPArray,coefficients::AbstractVector{Float64}, name::AbstractString="", value::Number=NaN) =
Variable(m, lower, upper, cat, objcoef, constraints.innerArray, coefficients, name, value)

# add variable to existing constraints
function Variable(m::Model,lower::Number,upper::Number,cat::Symbol,objcoef::Number,
constraints::Vector,coefficients::Vector{Float64}, name::AbstractString="", value::Number=NaN)
constraints::AbstractVector,coefficients::AbstractVector{Float64}, name::AbstractString="", value::Number=NaN)
for c in constraints
if !isa(c, LinConstrRef)
error("Unexpected constraint of type $(typeof(c)). Column-wise modeling only supported for linear constraints")
Expand Down Expand Up @@ -909,9 +909,9 @@ include("print.jl")
# Deprecations
include("deprecated.jl")

getvalue{T<:JuMPTypes}(arr::Array{T}) = map(getvalue, arr)
getvalue{T<:JuMPTypes}(arr::AbstractArray{T}) = map(getvalue, arr)
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Not needed, use getvalue.(arr) instead.

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So delete the method?

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It's been previously discussed. The conclusion for now is to allow getvalue on objects that JuMP creates, and JuMP can create Array{Variable}, e.g., PSD matrices.


function setvalue{T<:AbstractJuMPScalar}(set::Array{T}, val::Array)
function setvalue{T<:AbstractJuMPScalar}(set::AbstractArray{T}, val::AbstractArray)
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Also not needed, use setvalue.(set,val)

promote_shape(size(set), size(val)) # Check dimensions match
for I in eachindex(set)
setvalue(set[I], val[I])
Expand Down
2 changes: 1 addition & 1 deletion src/JuMPArray.jl
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Expand Up @@ -10,7 +10,7 @@ immutable JuMPArray{T,N,NT} <: JuMPContainer{T,N}
meta::Dict{Symbol,Any}
end

@generated function JuMPArray{T,N}(innerArray::Array{T,N}, indexsets::NTuple{N,Any})
@generated function JuMPArray{T,N}(innerArray::AbstractArray{T,N}, indexsets::NTuple{N,Any})
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I don't think this is needed

dicttuple = Expr(:tuple)
for i in 1:N
inner = quote
Expand Down
4 changes: 2 additions & 2 deletions src/affexpr.jl
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Expand Up @@ -181,10 +181,10 @@ function addconstraint(m::Model, c::LinearConstraint)
end
return LinConstrRef(m,length(m.linconstr))
end
addconstraint(m::Model, c::Array{LinearConstraint}) =
addconstraint(m::Model, c::AbstractArray{LinearConstraint}) =
error("The operators <=, >=, and == can only be used to specify scalar constraints. If you are trying to add a vectorized constraint, use the element-wise dot comparison operators (.<=, .>=, or .==) instead")

function addVectorizedConstraint(m::Model, v::Array{LinearConstraint})
function addVectorizedConstraint(m::Model, v::AbstractArray{LinearConstraint})
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This is an internal function, it's unclear how it could be called with an AbstractArray

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I'm somewhat uncertain if this method should be changed. Is there a compelling reason to pass a sparse matrix of linear constraints, for example?

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Without changing this method, the following fails:

m = Model()
v = @variable(m, [1:3])
x = OffsetArray(v, -3)
@constraint(m, x .== 0)

because there is no Convert method that constructs an Array from an OffsetArray (a conscious design decision in OffsetArrays).

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This doesn't seem like the right fix. I think constructconstraint!(x::Array, sense::Symbol) = map(c->constructconstraint!(c,sense), x) should be changed to return a flat Vector of constraints. Then there's no need to touch addVectorizedConstraint.

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Since this also doesn't work:

t = OffsetArray(rand(3), -3)
t .== 0

I'm not sure this is desirable behavior.

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OK, will change. @joehuchette, that seems like a bug in Base.

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Done.

ret = Array{LinConstrRef}(size(v))
for I in eachindex(v)
ret[I] = addconstraint(m, v[I])
Expand Down
16 changes: 8 additions & 8 deletions src/callbacks.jl
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Expand Up @@ -59,7 +59,7 @@ function addinfocallback(m::Model, f::Function; when::Symbol = _unspecifiedstate
push!(m.callbacks, InfoCallback(f, when))
end

function lazycallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::Vector{LazyCallback})
function lazycallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::AbstractVector{LazyCallback})
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Internal, won't be called with AbstractVector

state = MathProgBase.cbgetstate(d)
@assert state == :MIPSol || state == :MIPNode
anyfrac = mapreduce(|, cbs) do cb
Expand Down Expand Up @@ -92,7 +92,7 @@ function lazycallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::Vecto
:Continue
end

function attach_callbacks(m::Model, cbs::Vector{LazyCallback})
function attach_callbacks(m::Model, cbs::AbstractVector{LazyCallback})
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Internal, won't be called with AbstractVector

cb = d -> lazycallback(d,m,cbs)
if applicable(MathProgBase.setlazycallback!, m.internalModel, cb)
MathProgBase.setlazycallback!(m.internalModel, cb)
Expand All @@ -101,7 +101,7 @@ function attach_callbacks(m::Model, cbs::Vector{LazyCallback})
end
end

function cutcallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::Vector{CutCallback})
function cutcallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::AbstractVector{CutCallback})
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Internal, won't be called with AbstractVector

state = MathProgBase.cbgetstate(d)
@assert state == :MIPNode
MathProgBase.cbgetlpsolution(d,m.colVal)
Expand All @@ -119,7 +119,7 @@ function cutcallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::Vector
:Continue
end

function attach_callbacks(m::Model, cbs::Vector{CutCallback})
function attach_callbacks(m::Model, cbs::AbstractVector{CutCallback})
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Internal, won't be called with AbstractVector

cb = d -> cutcallback(d,m,cbs)
if applicable(MathProgBase.setcutcallback!, m.internalModel, cb)
MathProgBase.setcutcallback!(m.internalModel, cb)
Expand All @@ -129,7 +129,7 @@ function attach_callbacks(m::Model, cbs::Vector{CutCallback})
end


function heurcallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::Vector{HeuristicCallback})
function heurcallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::AbstractVector{HeuristicCallback})
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Internal, won't be called with AbstractVector

state = MathProgBase.cbgetstate(d)
@assert state == :MIPNode
MathProgBase.cbgetlpsolution(d,m.colVal)
Expand All @@ -147,7 +147,7 @@ function heurcallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::Vecto
:Continue
end

function attach_callbacks(m::Model, cbs::Vector{HeuristicCallback})
function attach_callbacks(m::Model, cbs::AbstractVector{HeuristicCallback})
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Internal, won't be called with AbstractVector

cb = d -> heurcallback(d,m,cbs)
if applicable(MathProgBase.setheuristiccallback!, m.internalModel, cb)
MathProgBase.setheuristiccallback!(m.internalModel, cb)
Expand All @@ -156,7 +156,7 @@ function attach_callbacks(m::Model, cbs::Vector{HeuristicCallback})
end
end

function infocallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::Vector{InfoCallback})
function infocallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::AbstractVector{InfoCallback})
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Internal, won't be called with AbstractVector

state = MathProgBase.cbgetstate(d)
if state == :MIPSol
MathProgBase.cbgetmipsolution(d,m.colVal)
Expand All @@ -179,7 +179,7 @@ function infocallback(d::MathProgBase.MathProgCallbackData, m::Model, cbs::Vecto
:Continue
end

function attach_callbacks(m::Model, cbs::Vector{InfoCallback})
function attach_callbacks(m::Model, cbs::AbstractVector{InfoCallback})
cb = d -> infocallback(d,m,cbs)
if applicable(MathProgBase.setinfocallback!, m.internalModel, cb)
MathProgBase.setinfocallback!(m.internalModel, cb)
Expand Down
8 changes: 4 additions & 4 deletions src/macros.jl
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Expand Up @@ -304,17 +304,17 @@ function constructconstraint!(normexpr::SOCExpr, sense::Symbol)
end
end

constructconstraint!(x::Array, sense::Symbol) = map(c->constructconstraint!(c,sense), x)
constructconstraint!(x::AbstractArray, sense::Symbol) = map(c->constructconstraint!(c,sense), x)

_vectorize_like(x::Number, y::Array{AffExpr}) = fill(x, size(y))
function _vectorize_like{R<:Number}(x::Array{R}, y::Array{AffExpr})
_vectorize_like(x::Number, y::AbstractArray{AffExpr}) = fill(x, size(y))
function _vectorize_like{R<:Number}(x::AbstractArray{R}, y::AbstractArray{AffExpr})
for i in 1:max(ndims(x),ndims(y))
size(x,i) == size(y,i) || error("Unequal sizes for ranged constraint")
end
x
end

function constructconstraint!(x::Array{AffExpr}, lb, ub)
function constructconstraint!(x::AbstractArray{AffExpr}, lb, ub)
LB = _vectorize_like(lb,x)
UB = _vectorize_like(ub,x)
ret = similar(x, LinearConstraint)
Expand Down
10 changes: 5 additions & 5 deletions src/nlp.jl
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Expand Up @@ -169,7 +169,7 @@ type NLPEvaluator <: MathProgBase.AbstractNLPEvaluator
end
end

function simplify_expression(nd::Vector{NodeData}, const_values, subexpression_linearity, fixed_variables, parameter_values, x_values, subexpression_values)
function simplify_expression(nd::AbstractVector{NodeData}, const_values, subexpression_linearity, fixed_variables, parameter_values, x_values, subexpression_values)
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None of the changes in this file are needed


adj = adjmat(nd)
forward_storage = zeros(length(nd))
Expand All @@ -180,7 +180,7 @@ function simplify_expression(nd::Vector{NodeData}, const_values, subexpression_l
return nd_new, forward_storage[1], linearity[1]
end

function FunctionStorage(nd::Vector{NodeData}, const_values,numVar, coloring_storage, want_hess::Bool, subexpr::Vector{Vector{NodeData}}, dependent_subexpressions, subexpression_linearity, subexpression_edgelist, subexpression_variables, fixed_variables)
function FunctionStorage(nd::AbstractVector{NodeData}, const_values,numVar, coloring_storage, want_hess::Bool, subexpr::AbstractVector{Vector{NodeData}}, dependent_subexpressions, subexpression_linearity, subexpression_edgelist, subexpression_variables, fixed_variables)

adj = adjmat(nd)
forward_storage = zeros(length(nd))
Expand Down Expand Up @@ -215,7 +215,7 @@ function FunctionStorage(nd::Vector{NodeData}, const_values,numVar, coloring_sto

end

function SubexpressionStorage(nd::Vector{NodeData}, const_values,numVar, fixed_variables,subexpression_linearity)
function SubexpressionStorage(nd::AbstractVector{NodeData}, const_values,numVar, fixed_variables,subexpression_linearity)

adj = adjmat(nd)
forward_storage = zeros(length(nd))
Expand All @@ -229,7 +229,7 @@ function SubexpressionStorage(nd::Vector{NodeData}, const_values,numVar, fixed_v

end

function MathProgBase.initialize(d::NLPEvaluator, requested_features::Vector{Symbol})
function MathProgBase.initialize(d::NLPEvaluator, requested_features::AbstractVector{Symbol})
for feat in requested_features
if !(feat in MathProgBase.features_available(d))
error("Unsupported feature $feat")
Expand Down Expand Up @@ -1081,7 +1081,7 @@ function Base.show(io::IO,s::SubexpressionPrintWrapper)
end

# we splat in the subexpressions (for now)
function tapeToExpr(m::Model, k, nd::Vector{NodeData}, adj, const_values, parameter_values, subexpressions::Vector{Any},user_operators::ReverseDiffSparse.UserOperatorRegistry, generic_variable_names::Bool, splat_subexpressions::Bool, print_mode=REPLMode)
function tapeToExpr(m::Model, k, nd::AbstractVector{NodeData}, adj, const_values, parameter_values, subexpressions::AbstractVector{Any},user_operators::ReverseDiffSparse.UserOperatorRegistry, generic_variable_names::Bool, splat_subexpressions::Bool, print_mode=REPLMode)

children_arr = rowvals(adj)

Expand Down
34 changes: 18 additions & 16 deletions src/norms.jl
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Expand Up @@ -40,24 +40,26 @@ end
Base.copy{P,C,V}(x::GenericNorm{P,C,V}) = GenericNorm{P,C,V}(copy(x.terms))

# Handle the norm() function by flattening arguments into a vector
Base.norm{V<:AbstractJuMPScalar}(x::V, p::Real=2) = vecnorm(x,p)
Base.norm{V<:AbstractJuMPScalar}(x::Array{V}, p::Real=2) = vecnorm(x,p)
Base.norm{V<:AbstractJuMPScalar}(x::JuMPArray{V},p::Real=2) = vecnorm(x,p)
Base.norm{V<:AbstractJuMPScalar}(x::JuMPDict{V}, p::Real=2) = vecnorm(x,p)
Base.norm{C,V}(x::GenericAffExpr{C,V}, p::Real=2) = vecnorm(x,p)
Base.norm{C,V}(x::Array{GenericAffExpr{C,V}}, p::Real=2) = vecnorm(x,p)
Base.norm{C,V}(x::JuMPArray{GenericAffExpr{C,V}},p::Real=2) = vecnorm(x,p)
Base.norm{C,V}(x::JuMPDict{GenericAffExpr{C,V}}, p::Real=2) = vecnorm(x,p)
Base.norm{V<:AbstractJuMPScalar}(x::V, p::Real=2) = vecnorm(x,p)
Base.norm{V<:AbstractJuMPScalar}(x::AbstractVector{V}, p::Real=2) = vecnorm(x,p)
Base.norm{V<:AbstractJuMPScalar}(x::AbstractMatrix{V}, p::Real=2) = vecnorm(x,p)
Base.norm{V<:AbstractJuMPScalar}(x::JuMPArray{V}, p::Real=2) = vecnorm(x,p)
Base.norm{V<:AbstractJuMPScalar}(x::JuMPDict{V}, p::Real=2) = vecnorm(x,p)
Base.norm{C,V}(x::GenericAffExpr{C,V}, p::Real=2) = vecnorm(x,p)
Base.norm{C,V}(x::AbstractVector{GenericAffExpr{C,V}}, p::Real=2) = vecnorm(x,p)
Base.norm{C,V}(x::AbstractMatrix{GenericAffExpr{C,V}}, p::Real=2) = vecnorm(x,p)
Base.norm{C,V}(x::JuMPArray{GenericAffExpr{C,V}}, p::Real=2) = vecnorm(x,p)
Base.norm{C,V}(x::JuMPDict{GenericAffExpr{C,V}}, p::Real=2) = vecnorm(x,p)

_vecaff(C,V,x) = map(GenericAffExpr{C,V},vec(x))
Base.vecnorm{V<:AbstractJuMPScalar}(x::V, p::Real=2) = GenericNorm(p, [GenericAffExpr{Float64,V}(x)] )
Base.vecnorm{V<:AbstractJuMPScalar}(x::Array{V}, p::Real=2) = GenericNorm(p, _vecaff(Float64,V,x) )
Base.vecnorm{V<:AbstractJuMPScalar}(x::JuMPArray{V},p::Real=2) = GenericNorm(p, _vecaff(Float64,V,x.innerArray) )
Base.vecnorm{V<:AbstractJuMPScalar}(x::JuMPDict{V}, p::Real=2) = GenericNorm(p, _vecaff(Float64,V,collect(values(x))) )
Base.vecnorm{C,V}(x::GenericAffExpr{C,V}, p::Real=2) = GenericNorm(p, [x])
Base.vecnorm{C,V}(x::Array{GenericAffExpr{C,V}}, p::Real=2) = GenericNorm(p, vec(x))
Base.vecnorm{C,V}(x::JuMPArray{GenericAffExpr{C,V}},p::Real=2) = GenericNorm(p, vec(x.innerArray))
Base.vecnorm{C,V}(x::JuMPDict{GenericAffExpr{C,V}}, p::Real=2) = GenericNorm(p, collect(values(x)))
Base.vecnorm{V<:AbstractJuMPScalar}(x::V, p::Real=2) = GenericNorm(p, [GenericAffExpr{Float64,V}(x)] )
Base.vecnorm{V<:AbstractJuMPScalar}(x::AbstractArray{V}, p::Real=2) = GenericNorm(p, _vecaff(Float64,V,x) )
Base.vecnorm{V<:AbstractJuMPScalar}(x::JuMPArray{V}, p::Real=2) = GenericNorm(p, _vecaff(Float64,V,x.innerArray) )
Base.vecnorm{V<:AbstractJuMPScalar}(x::JuMPDict{V}, p::Real=2) = GenericNorm(p, _vecaff(Float64,V,collect(values(x))) )
Base.vecnorm{C,V}(x::GenericAffExpr{C,V}, p::Real=2) = GenericNorm(p, [x])
Base.vecnorm{C,V}(x::AbstractArray{GenericAffExpr{C,V}}, p::Real=2) = GenericNorm(p, vec(x))
Base.vecnorm{C,V}(x::JuMPArray{GenericAffExpr{C,V}}, p::Real=2) = GenericNorm(p, vec(x.innerArray))
Base.vecnorm{C,V}(x::JuMPDict{GenericAffExpr{C,V}}, p::Real=2) = GenericNorm(p, collect(values(x)))

# Called by the parseNorm macro for e.g. norm2{...}
# If the arguments are tightly typed, just pass to the constructor
Expand Down
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