Adding global negotiation priority parameter. Adding tests for priorities.

Author: rcschrg

Project.toml

@@ -7,6 +7,7 @@ version = "0.1.0"
 [deps]
 AutoHashEquals = "15f4f7f2-30c1-5605-9d31-71845cf9641f"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+ECOS = "e2685f51-7e38-5353-a97d-a921fd2c8199"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Mango = "5e49fdec-d473-4d14-b295-7bff2fcf1925"
@@ -16,6 +17,7 @@ Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 [compat]
 AutoHashEquals = "2.2.0"
 Distributions = "0.25.115"
+ECOS = "1.1.3"
 JuMP = "1.25.0"
 LinearAlgebra = "1.11.0"
 Mango = "^0.4"

src/algorithm/admm/consensus_admm.jl

@@ -1,4 +1,4 @@
-export create_consensus_target_reach_admm_coordinator, ADMMConsensusGlobalActor, create_admm_start_with_target
+export create_consensus_target_reach_admm_coordinator, ADMMConsensusGlobalActor
 
 
 @kwdef struct ADMMConsensusGlobalActor <: ADMMGlobalActor
@@ -41,7 +41,3 @@ end
 function create_consensus_target_reach_admm_coordinator()
     return ADMMGenericCoordinator(global_actor=ADMMConsensusGlobalActor())
 end
-
-function create_admm_start_with_target(target::Vector{<:Real})
-    return ADMMStart(target, length(target))
-end

src/algorithm/admm/core.jl

@@ -1,10 +1,14 @@
-export ADMMStart, ADMMAnswer, ADMMAnswer, ADMMGlobalActor, ADMMGlobalObjective, ADMMGenericCoordinator
+export ADMMStart, ADMMAnswer, ADMMAnswer, ADMMGlobalActor, ADMMGlobalObjective, ADMMGenericCoordinator, create_admm_start
 
 struct ADMMStart
     data::Any
     solution_length::Int
 end
 
+function create_admm_start(data::Any, length::Int)
+    return ADMMStart(data, length)
+end
+
 struct ADMMMessage 
     v::Vector{Float64}
     ρ::Float64
@@ -49,7 +53,7 @@ end
 @kwdef struct ADMMGenericCoordinator <: Coordinator
     global_actor::ADMMGlobalActor
     ρ::Float64 = 1.0 
-    max_iters::Int64 = 100
+    max_iters::Int64 = 1000
     slack_penalty::Int64 = 100
     abs_tol::Float64 = 1e-4
     rel_tol::Float64 = 1e-3
@@ -105,11 +109,11 @@ function _start_coordinator(admm::ADMMGenericCoordinator, carrier::Carrier, inpu
 
         # Varying penalty paramter according to B. S. He, H. Yang, and S. L. Wang, “Alternating direction method with self
         # adaptive penalty parameters for monotone variational inequalities,”
-        if r_norm > μ * s_norm
-            ρ = ρ * τ
-        elseif s_norm > μ * r_norm
-            ρ = ρ / τ
-        end
+        # if r_norm > μ * s_norm
+        #     ρ = ρ * τ
+        # elseif s_norm > μ * r_norm
+        #     ρ = ρ / τ
+        # end
 
         if k == max_iters
             @warn "Reached max iterations ($max_iters) without full convergence."
@@ -122,4 +126,4 @@ end
 function start_optimization(coordinator::ADMMGenericCoordinator, carrier::Carrier, start_data::ADMMStart, meta::Any)
     x,_,_ = _start_coordinator(coordinator, carrier, start_data.data, start_data.solution_length)
     return x
-end
+end

src/algorithm/admm/flex_actor.jl

@@ -9,13 +9,13 @@ mutable struct ADMMFlexActor <: DistributedAlgorithm
     u::Vector{Float64} # upper bounds
     C::Matrix{Float64} # coupling matrix
     d::Vector{Float64} # coupling RHS
-    S::Vector{Float64} # prio penalty per sector
+    S::Vector{<:Real} # prio penalty per sector
     x::Vector{Float64} # intermediate result/result
     ADMMFlexActor(l::Vector{Float64},
         u::Vector{Float64},
         C::Matrix{Float64},
         d::Vector{Float64},
-        S::Vector{Float64}) = new(l, u, C, d, S, Vector{Float64}())
+        S::Vector{<:Real}) = new(l, u, C, d, S, Vector{Float64}())
 end
 
 function _create_C_and_d(u::Vector{<:Real})
@@ -44,17 +44,18 @@ function _create_C_and_d(u::Vector{<:Real})
     return C, d
 end
 
-function create_admm_flex_actor_one_to_many(in_capacity::Real, η::Vector{Float64}, S::Union{Nothing,Vector{<:Real}}=nothing)
+function create_admm_flex_actor_one_to_many(in_capacity::Real, η::Vector{Float64}, P::Union{Nothing,Vector{<:Real}}=nothing)
     tech_capacity = in_capacity .* η
 
-    if isnothing(S)
-        S = zeros(length(η))
+    if isnothing(P)
+        P = zeros(length(η))
     end
     l = min.(zeros(length(tech_capacity)), tech_capacity)
     u = max.(tech_capacity, zeros(length(tech_capacity)))
     C, d = _create_C_and_d(tech_capacity)
 
-    return ADMMFlexActor(l, u, C, d, S)
+    # negative P to convert from penalty to reward
+    return ADMMFlexActor(l, u, C, d, -P)
 end
 
 function result(actor::ADMMFlexActor)

src/algorithm/admm/sharing_admm.jl

@@ -1,14 +1,16 @@
-export create_sharing_target_distance_admm_coordinator, ADMMSharingGlobalActor, ADMMTargetDistanceObjective
+export create_sharing_target_distance_admm_coordinator, ADMMSharingGlobalActor, ADMMTargetDistanceObjective, create_admm_sharing_data
 
 using JuMP
 using OSQP
 using LinearAlgebra
 
 struct ADMMTargetDistanceObjective <: ADMMGlobalObjective end
 
+# currently unused
 function objective(objective::ADMMTargetDistanceObjective, target::Vector{<:Real}, x, u, z, N)
     m = length(z)
 
+    #+ objective(actor.global_objective, input, x, u, N*z, N))
     # quadratic distance
     return sum((target[i] - z[i])^2 for i in 1:m)
 end
@@ -17,7 +19,24 @@ struct ADMMSharingGlobalActor <: ADMMGlobalActor
     global_objective::ADMMGlobalObjective
 end
 
-function z_update(actor::ADMMSharingGlobalActor, input::Any, x, u, z, ρ, N)
+struct ADMMSharingData
+    target::Vector{<:Real}
+    priorities::Vector{<:Real}
+end
+
+function create_admm_sharing_data(target::Vector{<:Real}, priorities::Union{Nothing,Vector{<:Real}}=nothing)
+    if isnothing(priorities)
+        priorities = ones(length(target))
+    end
+    # negative to turn penalty to priority
+    return ADMMSharingData(target, -priorities)
+end
+
+function create_admm_start(data::ADMMSharingData)
+    return ADMMStart(data, length(data.target))
+end
+
+function z_update(actor::ADMMSharingGlobalActor, input::ADMMSharingData, x, u, z, ρ, N)
     x_avg = sum(x) ./ length(x)
 
     m = length(x_avg)
@@ -26,9 +45,16 @@ function z_update(actor::ADMMSharingGlobalActor, input::Any, x, u, z, ρ, N)
     set_silent(model)
 
     @variable(model, z[1:m])
-    @objective(model, Min, (N*ρ/2)*sum((z[i] - u[i] - x_avg[i])^2 for i in 1:m) 
-                                + objective(actor.global_objective, input, x, u, N*z, N))
-    optimize!(model)    
+    @variable(model, d[1:m] >= 0)   # absolute value proxy
+
+    for i in 1:m
+        @constraint(model, d[i] >= input.priorities[i] * (N*z[i] - input.target[i]))
+        @constraint(model, d[i] >= - input.priorities[i] * (N*z[i] - input.target[i]))
+    end
+
+    @objective(model, Min, (N*ρ/2)*sum((z[i] - u[i] - x_avg[i])^2 for i in 1:m)
+                + sum(d[i] for i=1:m))
+    optimize!(model)
 
     return value.(z)
 end

test/sharing_admm_tests.jl

@@ -34,7 +34,7 @@ end
     auto_assign!(complete_topology(3, tid=:custom), container)
 
     activate(container) do
-        wait(send_message(c, StartCoordinatedDistributedOptimization(create_admm_start_with_target([2.0, 2.0, 3.0])), address(ca)))
+        wait(send_message(c, StartCoordinatedDistributedOptimization(create_admm_start(create_admm_sharing_data([2.0, 2.0, 3.0]))), address(ca)))
         wait(coord_role.task)
     end
 
@@ -70,7 +70,7 @@ end
     auto_assign!(complete_topology(3, tid=:custom), container)
 
     activate(container) do
-        wait(send_message(c, StartCoordinatedDistributedOptimization(create_admm_start_with_target([0.2, 1, -2])), address(ca)))
+        wait(send_message(c, StartCoordinatedDistributedOptimization(create_admm_start(create_admm_sharing_data([0.2, 1, -2]))), address(ca)))
         wait(coord_role.task)
     end
 
@@ -106,13 +106,122 @@ end
     auto_assign!(complete_topology(3, tid=:custom), container)
 
     activate(container) do
-        wait(send_message(c, StartCoordinatedDistributedOptimization(create_admm_start_with_target([1.2, 1, -4])), address(ca)))
+        wait(send_message(c, StartCoordinatedDistributedOptimization(create_admm_start(create_admm_sharing_data([1.2, 1, -4]))), address(ca)))
         wait(coord_role.task)
     end
 
-    @test isapprox(flex_actor.x, [0.1338046305205991, 0.6710353497283177, -1.3420877350612257], atol=1e-3)
-    @test isapprox(flex_actor2.x, [0.13380191440490008, 0.6710343387311162, -1.3420863674684713], atol=1e-3)
-    @test isapprox(flex_actor3.x, [1.1249597637572557, -1.079012246032314e-6, -1.1249611366933858], atol=1e-3)
+    @test isapprox(flex_actor.x, [0.1551348309982447, 0.7764857848417002, -1.5529580879935843], atol=1e-3)
+    @test isapprox(flex_actor2.x, [0.1551322355095896, 0.7764795912324005, -1.5529455908199763], atol=1e-3)
+    @test isapprox(flex_actor3.x, [0.8928358745473691, -1.2344094570466974e-6, -0.8928393030645658], atol=1e-3)
+    @test handle.got_it
+    @test handle2.got_it
+    @test handle3.got_it
+end
+
+@testset "TestFlexADMMAWithMang2" begin
+    container = create_tcp_container("127.0.0.1", 5555)
+
+    flex_actor = create_admm_flex_actor_one_to_many(10, [0.1, 0.5, -1])
+    flex_actor2 = create_admm_flex_actor_one_to_many(15, [0.1, 0.5, -1])
+    flex_actor3 = create_admm_flex_actor_one_to_many(10, [-1.0, 0.0, 1.0])
+    coordinator = create_sharing_target_distance_admm_coordinator()
+
+    dor = DistributedOptimizationRole(flex_actor, tid=:custom)
+    dor2 = DistributedOptimizationRole(flex_actor2, tid=:custom)
+    dor3 = DistributedOptimizationRole(flex_actor3, tid=:custom)
+    coord_role = CoordinatorRole(coordinator, tid=:custom, include_self=true)
+
+    handle = HandleOptimizationResultRole()
+    handle2 = HandleOptimizationResultRole()
+    handle3 = HandleOptimizationResultRole()
+
+    add_agent_composed_of(container, dor, handle)
+    c = add_agent_composed_of(container, dor2, handle2)
+    ca = add_agent_composed_of(container, coord_role, dor3, handle3)
+    
+    auto_assign!(complete_topology(3, tid=:custom), container)
+
+    activate(container) do
+        wait(send_message(c, StartCoordinatedDistributedOptimization(create_admm_start(create_admm_sharing_data([-4, 0, 6]))), address(ca)))
+        wait(coord_role.task)
+    end
+
+    @test isapprox(flex_actor.x, [0, 0, 0], atol=1e-3)
+    @test isapprox(flex_actor2.x, [0, 0, 0], atol=1e-3)
+    @test isapprox(flex_actor3.x, [-5.6179147130732225, 0, 5.617914767964074], atol=1e-3)
+    @test handle.got_it
+    @test handle2.got_it
+    @test handle3.got_it
+end
+
+
+@testset "TestFlexADMMAWithMangoPriosThird" begin
+    container = create_tcp_container("127.0.0.1", 5555)
+
+    flex_actor = create_admm_flex_actor_one_to_many(10, [0.1, 0.5, -1])
+    flex_actor2 = create_admm_flex_actor_one_to_many(15, [0.1, 0.5, -1])
+    flex_actor3 = create_admm_flex_actor_one_to_many(10, [-1.0, 0.0, 1.0])
+    coordinator = create_sharing_target_distance_admm_coordinator()
+
+    dor = DistributedOptimizationRole(flex_actor, tid=:custom)
+    dor2 = DistributedOptimizationRole(flex_actor2, tid=:custom)
+    dor3 = DistributedOptimizationRole(flex_actor3, tid=:custom)
+    coord_role = CoordinatorRole(coordinator, tid=:custom, include_self=true)
+
+    handle = HandleOptimizationResultRole()
+    handle2 = HandleOptimizationResultRole()
+    handle3 = HandleOptimizationResultRole()
+
+    add_agent_composed_of(container, dor, handle)
+    c = add_agent_composed_of(container, dor2, handle2)
+    ca = add_agent_composed_of(container, coord_role, dor3, handle3)
+    
+    auto_assign!(complete_topology(3, tid=:custom), container)
+
+    activate(container) do
+        wait(send_message(c, StartCoordinatedDistributedOptimization(create_admm_start(create_admm_sharing_data([-4, 0, 6], [1,1,5]))), address(ca)))
+        wait(coord_role.task)
+    end
+
+    @test isapprox(flex_actor.x, [0.00018920502370025307, -6.385117748282918e-5, 0.0001192856645796826], atol=1e-3)
+    @test isapprox(flex_actor2.x, [0.00018947342781542052, -6.398798323709664e-5, 0.00011955174808086514], atol=1e-3)
+    @test isapprox(flex_actor3.x, [-6.024383392342817, -8.105452791414886e-7, 6.02438348197089], atol=1e-3)
+    @test handle.got_it
+    @test handle2.got_it
+    @test handle3.got_it
+end
+
+@testset "TestFlexADMMAWithMangoPriosFirst" begin
+    container = create_tcp_container("127.0.0.1", 5555)
+
+    flex_actor = create_admm_flex_actor_one_to_many(10, [0.1, 0.5, -1])
+    flex_actor2 = create_admm_flex_actor_one_to_many(15, [0.1, 0.5, -1])
+    flex_actor3 = create_admm_flex_actor_one_to_many(10, [-1.0, 0.0, 1.0])
+    coordinator = create_sharing_target_distance_admm_coordinator()
+
+    dor = DistributedOptimizationRole(flex_actor, tid=:custom)
+    dor2 = DistributedOptimizationRole(flex_actor2, tid=:custom)
+    dor3 = DistributedOptimizationRole(flex_actor3, tid=:custom)
+    coord_role = CoordinatorRole(coordinator, tid=:custom, include_self=true)
+
+    handle = HandleOptimizationResultRole()
+    handle2 = HandleOptimizationResultRole()
+    handle3 = HandleOptimizationResultRole()
+
+    add_agent_composed_of(container, dor, handle)
+    c = add_agent_composed_of(container, dor2, handle2)
+    ca = add_agent_composed_of(container, coord_role, dor3, handle3)
+    
+    auto_assign!(complete_topology(3, tid=:custom), container)
+
+    activate(container) do
+        wait(send_message(c, StartCoordinatedDistributedOptimization(create_admm_start(create_admm_sharing_data([-4, 0, 6], [5,1,1]))), address(ca)))
+        wait(coord_role.task)
+    end
+
+    @test isapprox(flex_actor.x, [0.00018920502370025307, -6.385117748282918e-5, 0.0001192856645796826], atol=1e-3)
+    @test isapprox(flex_actor2.x, [0.00018947342781542052, -6.398798323709664e-5, 0.00011955174808086514], atol=1e-3)
+    @test isapprox(flex_actor3.x, [-3.9830282351073403, -7.203077063324391e-7, 3.98302823714599], atol=1e-3)
     @test handle.got_it
     @test handle2.got_it
     @test handle3.got_it