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Initial implementation of Constrained GlobalOptimisation #33

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Initial implementation of Constrained GlobalOptimisation #33

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6 changes: 3 additions & 3 deletions src/IntervalOptimisation.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -5,13 +5,13 @@ module IntervalOptimisation
export minimise, maximise,
minimize, maximize,
minimise1d, minimise1d_deriv,
minimise_icp, minimise_icp_constrained
minimise_icp, minimise_icp_constrained, ConstraintCond

include("SortedVectors.jl")
using .SortedVectors

using IntervalArithmetic, IntervalRootFinding, DataStructures, IntervalConstraintProgramming, ForwardDiff

using IntervalArithmetic, DataStructures, IntervalConstraintProgramming, ForwardDiff
using ModelingToolkit

include("optimise.jl")
include("optimise1.jl")
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49 changes: 31 additions & 18 deletions src/optimise1.jl
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Expand Up @@ -82,26 +82,22 @@ struct IntervalBoxMinimum{N, T<:Real}
minimum::T
end

"""
Datatype to provide constraints to Global Optimisation such as:
```
Constraint(x->(x^2 - 10), -∞..1)
```
"""
struct Constraint{T<:Real}
f::Function
c::Interval{T}
end


Base.isless(a::IntervalBoxMinimum{N, T}, b::IntervalBoxMinimum{N, T}) where {N, T<:Real} = isless(a.minimum, b.minimum)

function minimise_icp(f::Function, x::IntervalBox{N, T}; reltol=1e-3, abstol=1e-3) where {N, T<:Real}
function minimise_icp(f::Function, x::IntervalBox{N, T}, var ; reltol=1e-3, abstol=1e-3) where {N, T<:Real}

Q = binary_minheap(IntervalBoxMinimum{N, T})

global_minimum = ∞

x = icp(f, x, -∞..global_minimum)
variables = []
for i in var append!(variables, Unknown(i)) end

f_op = f(variables...)
C = Contractor(f_op)
x = C(-∞..global_minimum, x)

arg_minima = IntervalBox{N, T}[]

Expand All @@ -125,7 +121,8 @@ function minimise_icp(f::Function, x::IntervalBox{N, T}; reltol=1e-3, abstol=1e-
global_minimum = current_minimum
end

X = icp(f, p.interval, -∞..global_minimum)
C = Contractor(f_op)
X = C(-∞..global_minimum, p.interval)

if diam(p.interval) < abstol
push!(arg_minima, p.interval)
Expand All @@ -141,17 +138,30 @@ function minimise_icp(f::Function, x::IntervalBox{N, T}; reltol=1e-3, abstol=1e-
return lb..global_minimum, arg_minima
end

function minimise_icp_constrained(f::Function, x::IntervalBox{N, T}, constraints::Vector{Constraint{T}} = Vector{Constraint{T}}(); reltol=1e-3, abstol=1e-3) where {N, T<:Real}

struct ConstraintCond{T}
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Why not just Constraint?

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Their might be more than one constraint condition thats why we need to built vector of such conditions.

f ::Operation
c ::Interval{T}
end


function minimise_icp_constrained(f::Function, x::IntervalBox{N,T}, var, constraints::Vector{ConstraintCond{T}} = Vector{ConstraintCond{T}}(); reltol=1e-3, abstol=1e-3) where {N, T<:Real}

Q = binary_minheap(IntervalBoxMinimum{N, T})

global_minimum = ∞

for t in constraints
x = icp(t.f, x, t.c)
C = Contractor(t.f, var)
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It is expensive to create a Contractor, so this should be done only once for each constraint.

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Oh right. We should built contractor for each constraint condition only once.

x = C(t.c, x)
end

x = icp(f, x, -∞..global_minimum)
variables = []
for i in var append!(variables, Unknown(i)) end

f_op = f(variables...)
C = Contractor(f_op)
x = C(-∞..global_minimum, x)

arg_minima = IntervalBox{N, T}[]

Expand All @@ -176,10 +186,13 @@ function minimise_icp_constrained(f::Function, x::IntervalBox{N, T}, constraints
global_minimum = current_minimum
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In general this logic is not correct, since there may be no feasible points in p.

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Yes right..!! this required more work need to be done on this case.

end

X = icp(f, p.interval, -∞..global_minimum)
C = Contractor(f_op)
X = C(-∞..global_minimum, p.interval)

for t in constraints
X = icp(t.f, X, t.c)
C = Contractor(t.f, var)
A = Interval(a)
X = C(A, X)
end

if diam(p.interval) < abstol
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