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nlp.jl
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nlp.jl
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################################################################################
# DATATYPES
################################################################################
# Extend addchild to take the root of another graph as input
function _LCRST.addchild(parent::_LCRST.Node{T}, newc::_LCRST.Node{T}) where T
# copy the new node if it is not a root
# otherwise, we are just merging 2 graphs together
if !AbstractTrees.isroot(newc)
newc = copy(newc)
end
# add it on to the tree
newc.parent = parent
prevc = parent.child
if prevc == parent
parent.child = newc
else
prevc = _LCRST.lastsibling(prevc)
prevc.sibling = newc
end
return newc
end
# Extend addchild with convenient nothing dispatch for empty previous child
function _LCRST.addchild(
parent::_LCRST.Node{T},
oldc::Nothing,
newc::_LCRST.Node{T}
) where T
return _LCRST.addchild(parent, newc)
end
# Extend addchild to efficiently add multiple children if the previous is known
function _LCRST.addchild(
parent::_LCRST.Node{T},
prevc::_LCRST.Node{T},
data::T
) where T
# add it on to the tree
newc = _LCRST.Node(data, parent)
prevc.sibling = newc
return newc
end
# Extend addchild to efficiently add multiple children if the previous is known
function _LCRST.addchild(
parent::_LCRST.Node{T},
prevc::_LCRST.Node{T},
newc::_LCRST.Node{T}
) where T
# check if the prev is actually a child of the parent
@assert prevc.parent === parent "Previous child doesn't belong to parent."
# copy the new node if it is not a root
# otherwise, we are just merging 2 graphs together
if !AbstractTrees.isroot(newc)
newc = copy(newc)
end
# add it on to the tree
newc.parent = parent
prevc.sibling = newc
return newc
end
# Map a LCRST tree based by operating each node with a function
function _map_tree(map_func::Function, node::_LCRST.Node)
new_node = map_func(node)
prev = nothing
for child in node
prev = _LCRST.addchild(new_node, prev, _map_tree(map_func, child))
end
return new_node
end
# Extend copying for graph nodes
function Base.copy(node::_LCRST.Node)
return _map_tree(n -> _LCRST.Node(n.data), node)
end
# Replace a node with its only child if it only has 1 child
function _merge_parent_and_child(node::_LCRST.Node)
if _LCRST.islastsibling(node.child)
child = node.child
node.data = child.data
for n in child
n.parent = node
end
node.child = child.child
child.child = child
child.parent = child
end
return node
end
# This is ambiguous but faster than the concrete alternatives tested so far
# Even better than using Node{Any}...
"""
NodeData
A `DataType` for storing values in an expression tree that is used in a
[`NLPExpr`](@ref). Acceptable value types include:
- `Real`: Constants
- `GeneralVariableRef`: Optimization variables
- `JuMP.GenericAffExpr{Float64, GeneralVariableRef}`: Affine expressions
- `JuMP.GenericQuadExpr{Float64, GeneralVariableRef}`: Quadratic expressions
- `Symbol`: Registered NLP function name.
**Fields**
- `value`: The stored value.
"""
struct NodeData
value
end
# Getter function for the node value (so it is easy to change later on if needed)
function _node_value(data::NodeData)
return data.value
end
# Recursively determine if node is effectively zero
function _is_zero(node::_LCRST.Node{NodeData})
raw = _node_value(node.data)
if isequal(raw, 0)
return true
elseif _LCRST.isleaf(node)
return false
elseif raw in (:+, :-) && all(_is_zero(n) for n in node)
return true
elseif raw == :* && any(_is_zero(n) for n in node)
return true
elseif raw in (:/, :^) && _is_zero(node.child)
return true
elseif all(_is_zero(n) for n in node) && iszero(get(_NativeNLPFunctions, (raw, length(collect(node))), (i...) -> true)((0.0 for n in node)...))
return true
else
return false
end
end
# Prone any nodes that are effectively zero
function _drop_zeros!(node::_LCRST.Node{NodeData})
if _LCRST.isleaf(node)
return node
elseif _is_zero(node)
node.data = NodeData(0.0)
_LCRST.makeleaf!(node)
return node
end
raw = _node_value(node.data)
if raw == :+
for n in node
if _is_zero(n)
_LCRST.prunebranch!(n)
end
end
_merge_parent_and_child(node)
elseif raw == :-
if _is_zero(node.child)
_LCRST.prunebranch!(node.child)
elseif _is_zero(node.child.sibling)
_LCRST.prunebranch!(node.child.sibling)
_merge_parent_and_child(node)
end
end
for n in node
_drop_zeros!(n)
end
return node
end
# Extend Base.isequal for our node types
function Base.isequal(n1::_LCRST.Node{NodeData}, n2::_LCRST.Node{NodeData})
isequal(_node_value(n1.data), _node_value(n2.data)) || return false
count(i -> true, n1) != count(i -> true, n2) && return false
for (c1, c2) in zip(n1, n2)
if !isequal(c1, c2)
return false
end
end
return true
end
"""
NLPExpr <: JuMP.AbstractJuMPScalar
A `DataType` for storing scalar nonlinear expressions. It stores the expression
algebraically via an expression tree where each node contains [`NodeData`](@ref)
that can store one of the following:
- a registered function name (stored as a `Symbol`)
- a constant
- a variable
- an affine expression
- a quadratic expression.
Specifically, it employs a left-child right-sibling tree
(from `LeftChildRightSiblingTrees.jl`) to represent the expression tree.
**Fields**
- `tree_root::LeftChildRightSiblingTrees.Node{NodeData}`: The root node of the
expression tree.
"""
struct NLPExpr <: JuMP.AbstractJuMPScalar
tree_root::_LCRST.Node{NodeData}
# Constructor
function NLPExpr(tree_root::_LCRST.Node{NodeData})
return new(tree_root)
end
end
# Extend basic functions
Base.broadcastable(nlp::NLPExpr) = Ref(nlp)
Base.copy(nlp::NLPExpr) = NLPExpr(copy(nlp.tree_root))
Base.zero(::Type{NLPExpr}) = NLPExpr(_LCRST.Node(NodeData(0.0)))
Base.one(::Type{NLPExpr}) = NLPExpr(_LCRST.Node(NodeData(1.0)))
function Base.isequal(nlp1::NLPExpr, nlp2::NLPExpr)
return isequal(nlp1.tree_root, nlp2.tree_root)
end
"""
JuMP.drop_zeros!(nlp::NLPExpr)::NLPExpr
Removes the zeros (possibly introduced by deletion) from an nonlinear expression.
Note this only uses a few simple heuristics and will not remove more complex
relationships like `cos(π/2)`.
**Example**
```julia-repl
julia> expr = x^2.3 * max(0, zero(NLPExpr)) - exp(1/x + 0)
x^2.3 * max(0, 0) - exp(1 / x + 0)
julia> drop_zeros!(expr)
-exp(1 / x)
```
"""
function JuMP.drop_zeros!(nlp::NLPExpr)
_drop_zeros!(nlp.tree_root) # uses a basic simplification scheme
return nlp
end
# Extend JuMP.isequal_canonical (uses some heuristics but is not perfect)
function JuMP.isequal_canonical(nlp1::NLPExpr, nlp2::NLPExpr)
n1 = _drop_zeros!(copy(nlp1.tree_root))
n2 = _drop_zeros!(copy(nlp2.tree_root))
return isequal(n1, n2)
end
# Print the tree structure of the expression tree
function print_expression_tree(io::IO, nlp::NLPExpr)
return AbstractTrees.print_tree(io, nlp.tree_root)
end
print_expression_tree(io::IO, expr) = println(io, expr)
"""
print_expression_tree(nlp::NLPExpr)
Print a tree representation of the nonlinear expression `nlp`.
**Example**
```julia-repl
julia> expr = (x * sin(x)^3) / 2
(x * sin(x)^3) / 2
julia> print_expression_tree(expr)
/
├─ *
│ ├─ x
│ └─ ^
│ ├─ sin
│ │ └─ x
│ └─ 3
└─ 2
```
"""
print_expression_tree(nlp::NLPExpr) = print_expression_tree(stdout::IO, nlp)
# Convenient expression alias
const AbstractInfOptExpr = Union{
NLPExpr,
JuMP.GenericQuadExpr{Float64, GeneralVariableRef},
JuMP.GenericAffExpr{Float64, GeneralVariableRef},
GeneralVariableRef
}
## Dispatch function for ast mapping
# Constant
function _ast_process_node(map_func::Function, c)
return c
end
# Variable
function _ast_process_node(map_func::Function, v::GeneralVariableRef)
return map_func(v)
end
# AffExpr
function _ast_process_node(map_func::Function, aff::JuMP.GenericAffExpr)
ex = Expr(:call, :+)
for (v, c) in aff.terms
if isone(c)
push!(ex.args, map_func(v))
else
push!(ex.args, Expr(:call, :*, c, map_func(v)))
end
end
if !iszero(aff.constant)
push!(ex.args, aff.constant)
end
return ex
end
# QuadExpr
function _ast_process_node(map_func::Function, quad::JuMP.GenericQuadExpr)
ex = Expr(:call, :+)
for (xy, c) in quad.terms
if isone(c)
push!(ex.args, Expr(:call, :*, map_func(xy.a), map_func(xy.b)))
else
push!(ex.args, Expr(:call, :*, c, map_func(xy.a), map_func(xy.b)))
end
end
append!(ex.args, _ast_process_node(map_func, quad.aff).args[2:end])
return ex
end
# Map an expression tree to a Julia AST tree that is compatible with JuMP
function _tree_map_to_ast(map_func::Function, node::_LCRST.Node)
if _LCRST.isleaf(node)
return _ast_process_node(map_func, _node_value(node.data))
else
ex = Expr(:call, _node_value(node.data)) # will be function symbol name
append!(ex.args, (_tree_map_to_ast(map_func, n) for n in node))
return ex
end
end
"""
map_nlp_to_ast(map_func::Function, nlp::NLPExpr)::Expr
Map the nonlinear expression `nlp` to a Julia AST expression where each variable
is mapped via `map_func` and is directly interpolated into the AST expression.
This is intended as an internal method that can be helpful for developers that
wish to map a `NLPExpr` to a Julia AST expression that is compatible with
`JuMP.add_NL_expression`.
"""
function map_nlp_to_ast(map_func::Function, nlp::NLPExpr)
return _tree_map_to_ast(map_func, nlp.tree_root)
end
################################################################################
# EXPRESSION CREATION HELPERS
################################################################################
## Make convenient dispatch methods for raw child input
# NLPExpr
function _process_child_input(nlp::NLPExpr)
return nlp.tree_root
end
# An InfiniteOpt expression (not general nonlinear)
function _process_child_input(v::AbstractInfOptExpr)
return NodeData(v)
end
# Function symbol
function _process_child_input(f::Symbol)
return NodeData(f)
end
# A constant
function _process_child_input(c::Union{Real, Bool})
return NodeData(c)
end
# Fallback
function _process_child_input(v)
error("Unrecognized algebraic expression input `$v`.")
end
# Generic graph builder
function _call_graph(func::Symbol, arg1, args...)
root = _LCRST.Node(NodeData(func))
prevc = _LCRST.addchild(root, _process_child_input(arg1))
for a in args
prevc = _LCRST.addchild(root, prevc, _process_child_input(a))
end
return root
end
################################################################################
# SUMS AND PRODUCTS
################################################################################
## Define helper functions for sum reductions
# Container of NLPExprs
function _reduce_by_first(::typeof(sum), first_itr::NLPExpr, itr, orig_itr; kws...)
for kw in kws
error("Unexpected keyword argument `$kw`.")
end
root = _LCRST.Node(NodeData(:+))
prevc = _LCRST.addchild(root, first_itr.tree_root)
for ex in itr
prevc = _LCRST.addchild(root, prevc, _process_child_input(ex))
end
return NLPExpr(root)
end
# Container of InfiniteOpt exprs
function _reduce_by_first(
::typeof(sum),
first_itr::JuMP.AbstractJuMPScalar,
itr,
orig_itr;
kws...
)
for kw in kws
error("Unexpected keyword argument `$kw`.")
end
result = first_itr
for i in itr
result = _MA.operate!!(_MA.add_mul, result, i)
end
return result
end
# Fallback
function _reduce_by_first(::typeof(sum), first_itr, itr, orig_itr; kws...)
return sum(identity, orig_itr; kws...)
end
# Hyjack Base.sum for better efficiency on iterators --> this is type piracy...
function Base.sum(itr::Base.Generator; kws...)
isempty(itr) && return sum(identity, itr; kws...)
itr1, new_itr = Iterators.peel(itr)
return _reduce_by_first(sum, itr1, new_itr, itr; kws...)
end
# Extend Base.sum for container of NLPExprs
function Base.sum(arr::AbstractArray{<:NLPExpr}; init = zero(NLPExpr))
isempty(arr) && return init
itr1, new_itr = Iterators.peel(arr)
return _reduce_by_first(sum, itr1, new_itr, arr)
end
# Extend Base.sum for container of InfiniteOpt exprs
function Base.sum(
arr::AbstractArray{<:AbstractInfOptExpr};
init = zero(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
)
isempty(arr) && return init
result = _MA.Zero()
for i in arr
result = _MA.operate!!(_MA.add_mul, result, i)
end
return result
end
## Define helper functions for reducing products
# Container of InfiniteOpt exprs
function _reduce_by_first(::typeof(prod), first_itr::AbstractInfOptExpr, itr, orig_itr; kws...)
for kw in kws
error("Unexpected keyword argument `$kw`.")
end
root = _LCRST.Node(NodeData(:*))
prevc = _LCRST.addchild(root, _process_child_input(first_itr))
for ex in itr
prevc = _LCRST.addchild(root, prevc, _process_child_input(ex))
end
return NLPExpr(root)
end
# Fallback
function _reduce_by_first(::typeof(prod), first_itr, itr, orig_itr; kws...)
return prod(identity, orig_itr; kws...)
end
# Hyjack Base.prod for better efficiency on iterators --> this is type piracy...
function Base.prod(itr::Base.Generator; kws...)
isempty(itr) && return prod(identity, itr; kws...)
itr1, new_itr = Iterators.peel(itr)
return _reduce_by_first(prod, itr1, new_itr, itr; kws...)
end
# Extend Base.prod for container of InfiniteOpt exprs
function Base.prod(arr::AbstractArray{<:AbstractInfOptExpr}; init = one(NLPExpr))
isempty(arr) && return init
itr1, new_itr = Iterators.peel(arr)
return _reduce_by_first(prod, itr1, new_itr, arr)
end
################################################################################
# MULTIPLICATION OPERATORS
################################################################################
# TODO more intelligently operate with constants
# QuadExpr * expr
function Base.:*(
quad::JuMP.GenericQuadExpr{Float64, GeneralVariableRef},
expr::AbstractInfOptExpr
)
return NLPExpr(_call_graph(:*, quad, expr))
end
# expr * QuadExpr
function Base.:*(
expr::AbstractInfOptExpr,
quad::JuMP.GenericQuadExpr{Float64, GeneralVariableRef}
)
return NLPExpr(_call_graph(:*, expr, quad))
end
# QuadExpr * QuadExpr
function Base.:*(
quad1::JuMP.GenericQuadExpr{Float64, GeneralVariableRef},
quad2::JuMP.GenericQuadExpr{Float64, GeneralVariableRef}
)
return NLPExpr(_call_graph(:*, quad1, quad2))
end
# NLPExpr * QuadExpr
function Base.:*(
nlp::NLPExpr,
quad::JuMP.GenericQuadExpr{Float64, GeneralVariableRef}
)
return NLPExpr(_call_graph(:*, nlp, quad))
end
# QuadExpr * NLPExpr
function Base.:*(
quad::JuMP.GenericQuadExpr{Float64, GeneralVariableRef},
nlp::NLPExpr
)
return NLPExpr(_call_graph(:*, quad, nlp))
end
# NLPExpr * expr/constant
function Base.:*(nlp::NLPExpr, expr::Union{AbstractInfOptExpr, Real})
return NLPExpr(_call_graph(:*, nlp, expr))
end
# expr/constant * NLPExpr
function Base.:*(expr::Union{AbstractInfOptExpr, Real}, nlp::NLPExpr)
return NLPExpr(_call_graph(:*, expr, nlp))
end
# NLPExpr * NLPExpr
function Base.:*(nlp1::NLPExpr, nlp2::NLPExpr)
return NLPExpr(_call_graph(:*, nlp1, nlp2))
end
# expr * expr * expr ...
function Base.:*(
expr1::AbstractInfOptExpr,
expr2::AbstractInfOptExpr,
expr3::AbstractInfOptExpr,
exprs::Vararg{AbstractInfOptExpr}
)
return NLPExpr(_call_graph(:*, expr1, expr2, expr3, exprs...))
end
# *NLPExpr
function Base.:*(nlp::NLPExpr)
return nlp
end
################################################################################
# DIVISION OPERATORS
################################################################################
# expr/constant / expr
function Base.:/(
expr1::Union{AbstractInfOptExpr, Real},
expr2::AbstractInfOptExpr
)
return NLPExpr(_call_graph(:/, expr1, expr2))
end
# NLPExpr / constant
function Base.:/(nlp::NLPExpr, c::Real)
if iszero(c)
error("Cannot divide by zero.")
elseif isone(c)
return nlp
else
return NLPExpr(_call_graph(:/, nlp, c))
end
end
################################################################################
# POWER OPERATORS
################################################################################
# expr ^ Integer
function Base.:^(expr::AbstractInfOptExpr, c::Integer)
if iszero(c)
return one(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
elseif isone(c)
return expr
elseif c == 2
return expr * expr
else
return NLPExpr(_call_graph(:^, expr, c))
end
end
# expr ^ Real
function Base.:^(expr::AbstractInfOptExpr, c::Real)
if iszero(c)
return one(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
elseif isone(c)
return expr
elseif c == 2
return expr * expr
else
return NLPExpr(_call_graph(:^, expr, c))
end
end
# NLPExpr ^ Integer
function Base.:^(expr::NLPExpr, c::Integer)
if iszero(c)
return one(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
elseif isone(c)
return expr
else
return NLPExpr(_call_graph(:^, expr, c))
end
end
# NLPExpr ^ Real
function Base.:^(expr::NLPExpr, c::Real)
if iszero(c)
return one(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
elseif isone(c)
return expr
else
return NLPExpr(_call_graph(:^, expr, c))
end
end
# expr/constant ^ expr
function Base.:^(
expr1::Union{AbstractInfOptExpr, Real},
expr2::AbstractInfOptExpr
)
return NLPExpr(_call_graph(:^, expr1, expr2))
end
################################################################################
# SUBTRACTION OPERATORS
################################################################################
# TODO more intelligently operate with constants
# NLPExpr - expr/constant
function Base.:-(nlp::NLPExpr, expr::Union{AbstractInfOptExpr, Real})
return NLPExpr(_call_graph(:-, nlp, expr))
end
# expr/constant - NLPExpr
function Base.:-(expr::Union{AbstractInfOptExpr, Real}, nlp::NLPExpr)
return NLPExpr(_call_graph(:-, expr, nlp))
end
# NLPExpr - NLPExpr
function Base.:-(nlp1::NLPExpr, nlp2::NLPExpr)
return NLPExpr(_call_graph(:-, nlp1, nlp2))
end
# -NLPExpr
function Base.:-(nlp::NLPExpr)
return NLPExpr(_call_graph(:-, nlp))
end
# Var - Var (to avoid using v == v)
function Base.:-(lhs::V, rhs::V) where {V<:GeneralVariableRef}
if isequal(lhs, rhs)
return zero(JuMP.GenericAffExpr{Float64,V})
else
return JuMP.GenericAffExpr(0.0,
DataStructures.OrderedDict(lhs => 1.0, rhs => -1.0))
end
end
################################################################################
# ADDITION OPERATORS
################################################################################
# TODO more intelligently operate with constants
# NLPExpr + expr/constant
function Base.:+(nlp::NLPExpr, expr::Union{AbstractInfOptExpr, Real})
return NLPExpr(_call_graph(:+, nlp, expr))
end
# expr/constant + NLPExpr
function Base.:+(expr::Union{AbstractInfOptExpr, Real}, nlp::NLPExpr)
return NLPExpr(_call_graph(:+, expr, nlp))
end
# NLPExpr + NLPExpr
function Base.:+(nlp1::NLPExpr, nlp2::NLPExpr)
return NLPExpr(_call_graph(:+, nlp1, nlp2))
end
# +NLPExpr
function Base.:+(nlp::NLPExpr)
return nlp
end
################################################################################
# MUTABLE ARITHMETICS
################################################################################
# Define NLPExpr as a mutable type for MA
_MA.mutability(::Type{NLPExpr}) = _MA.IsMutable()
# Extend MA.promote_operation for bettered efficiency
for type in (:Real, :GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}),
:(JuMP.GenericQuadExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(+),typeof(-),typeof(*),typeof(/),typeof(^)},
::Type{<:$type},
::Type{NLPExpr}
)
return NLPExpr
end
function _MA.promote_operation(
::Union{typeof(+),typeof(-),typeof(*),typeof(/),typeof(^)},
::Type{NLPExpr},
::Type{<:$type}
)
return NLPExpr
end
end
end
function _MA.promote_operation(
::Union{typeof(+),typeof(-),typeof(*),typeof(/),typeof(^)},
::Type{NLPExpr},
::Type{NLPExpr}
)
return NLPExpr
end
for type in (:GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(*),typeof(/),typeof(^)},
::Type{<:$type},
::Type{JuMP.GenericQuadExpr{Float64, GeneralVariableRef}}
)
return NLPExpr
end
function _MA.promote_operation(
::Union{typeof(*),typeof(/),typeof(^)},
::Type{JuMP.GenericQuadExpr{Float64, GeneralVariableRef}},
::Type{<:$type}
)
return NLPExpr
end
end
end
function _MA.promote_operation(
::Union{typeof(*),typeof(/),typeof(^)},
::Type{<:JuMP.GenericQuadExpr{Float64, GeneralVariableRef}},
::Type{<:JuMP.GenericQuadExpr{Float64, GeneralVariableRef}}
)
return NLPExpr
end
for type in (:GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}),
:(JuMP.GenericQuadExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(/),typeof(^)},
::Type{<:Real},
::Type{<:$type}
)
return NLPExpr
end
end
end
for type in (:GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(/),typeof(^)},
::Type{GeneralVariableRef},
::Type{<:$type}
)
return NLPExpr
end
end
end
for type in (:GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(/),typeof(^)},
::Type{JuMP.GenericAffExpr{Float64, GeneralVariableRef}},
::Type{<:$type}
)
return NLPExpr
end
end
end
# Extend MA.scaling in case an NLPExpr needs to be converted to a number
function _MA.scaling(nlp::NLPExpr)
c = _node_value(nlp.tree_root.data)
if !(c isa Real)
error("Cannot convert `$nlp` to `$Float64`.")
end
return _MA.scaling(c)
end
# Extend MA.mutable_copy to avoid unnecessary copying
function _MA.mutable_copy(nlp::NLPExpr)
return nlp # we don't need to copy since we build from the leaves up
end
# Extend MA.operate! as required
function _MA.operate!(
op::Union{typeof(zero), typeof(one)},
::NLPExpr
)
return op(NLPExpr) # not actually mutable for safety and efficiency
end
function _MA.operate!(
op::Union{typeof(+), typeof(-), typeof(*), typeof(/), typeof(^)},
nlp::NLPExpr,
v
)
return op(nlp, v)
end
function _MA.operate!(
op::Union{typeof(+), typeof(-), typeof(*), typeof(/), typeof(^)},
v,
nlp::NLPExpr
)
return op(v, nlp)
end
function _MA.operate!(
op::typeof(+),
v::Union{JuMP.GenericAffExpr{Float64, GeneralVariableRef},
JuMP.GenericQuadExpr{Float64, GeneralVariableRef}},
nlp::NLPExpr
)
return op(v, nlp)
end
function _MA.operate!(
op::typeof(-),
v::Union{JuMP.GenericAffExpr{Float64, GeneralVariableRef},
JuMP.GenericQuadExpr{Float64, GeneralVariableRef}},
nlp::NLPExpr
)
return op(v, nlp)
end
function _MA.operate!(
op::Union{typeof(+), typeof(-), typeof(*), typeof(/), typeof(^)},
nlp1::NLPExpr,
nlp2::NLPExpr
)
return op(nlp1, nlp2)
end
function _MA.operate!(op::_MA.AddSubMul, nlp::NLPExpr, args...)
return _MA.add_sub_op(op)(nlp, *(args...))
end
# TODO maybe extend _MA.add_mul/_MA_.sub_mul as well
################################################################################
# NATIVE NLP FUNCTIONS
################################################################################
# Store all of the native registered functions
const _NativeNLPFunctions = Dict{Tuple{Symbol, Int}, Function}(
(:-, 2) => -,
(:/, 2) => /,
(:^, 2) => ^
)
# List of 1 argument base functions to register
const _Base1ArgFuncList = (
:sqrt => sqrt,
:cbrt => cbrt,
:abs => abs,
:abs2 => abs2,
:inv => inv,
:log => log,
:log10 => log10,
:log2 => log2,
:log1p => log1p,
:exp => exp,
:exp2 => exp2,
:expm1 => expm1,
:sin => sin,
:cos => cos,
:tan => tan,
:sec => sec,
:csc => csc,
:cot => cot,
:sind => sind,
:cosd => cosd,
:tand => tand,
:secd => secd,
:cscd => cscd,
:cotd => cotd,
:asin => asin,
:acos => acos,
:atan => atan,
:asec => asec,
:acsc => acsc,
:acot => acot,
:asind => asind,
:acosd => acosd,
:atand => atand,
:asecd => asecd,
:acscd => acscd,
:acotd => acotd,
:sinh => sinh,
:cosh => cosh,
:tanh => tanh,
:sech => sech,
:csch => csch,
:coth => coth,
:asinh => asinh,
:acosh => acosh,
:atanh => atanh,
:asech => asech,
:acsch => acsch,
:acoth => acoth,
:deg2rad => deg2rad,
:rad2deg => rad2deg
)
# Setup the base 1 argument functions
for (name, func) in _Base1ArgFuncList
# add it to the main storage dict
_NativeNLPFunctions[(name, 1)] = func
# make an expression constructor
@eval begin
function Base.$name(v::AbstractInfOptExpr)
return NLPExpr(_call_graph($(quot(name)), v))
end
end
end
# Setup the ifelse function
_NativeNLPFunctions[(:ifelse, 3)] = Core.ifelse
"""
InfiniteOpt.ifelse(cond::NLPExpr, v1::Union{AbstractInfOptExpr, Real},
v2::Union{AbstractInfOptExpr, Real})::NLPExpr
A symbolic version of `Core.ifelse` that can be used to establish symbolic
expressions with logic conditions. Note that is must be written
`InfiniteOpt.ifelse` since it conflicts with `Core.ifelse`.
**Example**
```julia
julia> InfiniteOpt.ifelse(x >= y, 0, y^3)
ifelse(x >= y, 0, y^3)
```
"""
function ifelse(
cond::NLPExpr,
v1::Union{AbstractInfOptExpr, Real},
v2::Union{AbstractInfOptExpr, Real}
)
return NLPExpr(_call_graph(:ifelse, cond, v1, v2))
end
function ifelse(
cond::Bool,
v1::Union{AbstractInfOptExpr, Real},
v2::Union{AbstractInfOptExpr, Real}
)
return cond ? v1 : v2
end
# Setup the Base comparison functions
for (name, func) in (:< => Base.:(<), :(==) => Base.:(==), :> => Base.:(>),
:<= => Base.:(<=), :>= => Base.:(>=))
# add it to the main storage dict
_NativeNLPFunctions[(name, 2)] = func
# make an expression constructor
@eval begin
function Base.$name(v::AbstractInfOptExpr, c::Real)
return NLPExpr(_call_graph($(quot(name)), v, c))
end
function Base.$name(c::Real, v::AbstractInfOptExpr)
return NLPExpr(_call_graph($(quot(name)), c, v))
end
function Base.$name(v1::AbstractInfOptExpr, v2::AbstractInfOptExpr)
return NLPExpr(_call_graph($(quot(name)), v1, v2))
end
if $(quot(name)) in (:<, :>)
function Base.$name(v1::GeneralVariableRef, v2::GeneralVariableRef)
if isequal(v1, v2)
return false
else
return NLPExpr(_call_graph($(quot(name)), v1, v2))
end
end
else
function Base.$name(v1::GeneralVariableRef, v2::GeneralVariableRef)
if isequal(v1, v2)
return true
else