Initial converted project.

Author: rcschrg

distributed_resource_optimization/__init__.py

@@ -64,48 +64,11 @@ async def main():
 """
 
 # Carrier layer
-from .carrier.core import Carrier, EventWithValue
-from .carrier.simple import (
-    ActorContainer,
-    SimpleCarrier,
-    cid,
-    start_coordinated_optimization,
-    start_distributed_optimization,
-)
-
-# Algorithm base
-from .algorithm.core import (
-    CoordinatedDistributedAlgorithm,
-    Coordinator,
-    DistributedAlgorithm,
-    OptimizationMessage,
-    on_exchange_message,
-    start_optimization,
-)
-
-# COHDA
-from .algorithm.heuristic.cohda.core import (
-    COHDAAlgorithmData,
-    DefaultLocalDecider,
-    LocalDecider,
-    ScheduleSelection,
-    SolutionCandidate,
-    SystemConfig,
-    TargetParams,
-    WorkingMemory,
-    act,
-    cohda_default_performance,
-    create_cohda_participant,
-    create_cohda_participant_with_decider,
-    create_cohda_start_message,
-    create_from_updated_sysconf,
-    decide,
-    merge_candidates,
-    merge_sysconfigs,
-    perceive,
-    result as cohda_result,
+from .algorithm.admm.consensus_admm import (
+    ADMMConsensusGlobalActor,
+    create_admm_start_consensus,
+    create_consensus_target_reach_admm_coordinator,
 )
-from .algorithm.heuristic.cohda.decider import LocalSearchDecider
 
 # ADMM
 from .algorithm.admm.core import (
@@ -120,22 +83,21 @@ async def main():
 from .algorithm.admm.flex_actor import (
     ADMMFlexActor,
     create_admm_flex_actor_one_to_many,
-    result as admm_flex_result,
 )
-from .algorithm.admm.consensus_admm import (
-    ADMMConsensusGlobalActor,
-    create_admm_start_consensus,
-    create_consensus_target_reach_admm_coordinator,
+from .algorithm.admm.flex_actor import (
+    result as admm_flex_result,
 )
 from .algorithm.admm.sharing_admm import (
     ADMMSharingData,
     ADMMSharingGlobalActor,
     ADMMTargetDistanceObjective,
     create_admm_sharing_data,
-    create_admm_start as create_sharing_admm_start,
     create_sharing_admm_coordinator,
     create_sharing_target_distance_admm_coordinator,
 )
+from .algorithm.admm.sharing_admm import (
+    create_admm_start as create_sharing_admm_start,
+)
 
 # Consensus
 from .algorithm.consensus.averaging import (
@@ -151,6 +113,50 @@ async def main():
     LinearCostEconomicDispatchConsensusActor,
 )
 
+# Algorithm base
+from .algorithm.core import (
+    CoordinatedDistributedAlgorithm,
+    Coordinator,
+    DistributedAlgorithm,
+    OptimizationMessage,
+    on_exchange_message,
+    start_optimization,
+)
+
+# COHDA
+from .algorithm.heuristic.cohda.core import (
+    COHDAAlgorithmData,
+    DefaultLocalDecider,
+    LocalDecider,
+    ScheduleSelection,
+    SolutionCandidate,
+    SystemConfig,
+    TargetParams,
+    WorkingMemory,
+    act,
+    cohda_default_performance,
+    create_cohda_participant,
+    create_cohda_participant_with_decider,
+    create_cohda_start_message,
+    create_from_updated_sysconf,
+    decide,
+    merge_candidates,
+    merge_sysconfigs,
+    perceive,
+)
+from .algorithm.heuristic.cohda.core import (
+    result as cohda_result,
+)
+from .algorithm.heuristic.cohda.decider import LocalSearchDecider
+from .carrier.core import Carrier, EventWithValue
+from .carrier.simple import (
+    ActorContainer,
+    SimpleCarrier,
+    cid,
+    start_coordinated_optimization,
+    start_distributed_optimization,
+)
+
 # Mango carrier (optional — only imported if mango-agents is available)
 _MANGO_AVAILABLE = False
 try:

distributed_resource_optimization/algorithm/admm/__init__.py

@@ -1,3 +1,8 @@
+from .consensus_admm import (
+    ADMMConsensusGlobalActor,
+    create_admm_start_consensus,
+    create_consensus_target_reach_admm_coordinator,
+)
 from .core import (
     ADMMAnswer,
     ADMMGenericCoordinator,
@@ -8,11 +13,6 @@
     create_admm_start,
 )
 from .flex_actor import ADMMFlexActor, create_admm_flex_actor_one_to_many
-from .consensus_admm import (
-    ADMMConsensusGlobalActor,
-    create_admm_start_consensus,
-    create_consensus_target_reach_admm_coordinator,
-)
 from .sharing_admm import (
     ADMMSharingData,
     ADMMSharingGlobalActor,

distributed_resource_optimization/algorithm/admm/consensus_admm.py

@@ -17,7 +17,6 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any
 
 import numpy as np
 

distributed_resource_optimization/algorithm/admm/flex_actor.py

@@ -32,7 +32,7 @@
 class ADMMFlexActor(DistributedAlgorithm):
     """Local ADMM actor that solves a box+coupling-constrained QP.
 
-    :param l: Lower-bound vector.
+    :param lb: Lower-bound vector.
     :param u: Upper-bound vector.
     :param C: Coupling constraint matrix (rows: constraints, cols: variables).
     :param d: Coupling RHS vector.
@@ -41,13 +41,13 @@ class ADMMFlexActor(DistributedAlgorithm):
 
     def __init__(
         self,
-        l: np.ndarray,
+        lb: np.ndarray,
         u: np.ndarray,
         C: np.ndarray,
         d: np.ndarray,
         S: np.ndarray,
     ) -> None:
-        self.l = l
+        self.lb = lb
         self.u = u
         self.C = C
         self.d = d
@@ -79,7 +79,7 @@ def _local_update(actor: ADMMFlexActor, v: np.ndarray, rho: float) -> np.ndarray
     )
 
     constraints = [
-        x_var >= actor.l,
+        x_var >= actor.lb,
         x_var <= actor.u,
         actor.C @ x_var <= actor.d,
     ]
@@ -148,8 +148,8 @@ def create_admm_flex_actor_one_to_many(
 
     p_arr = np.zeros(len(eta_arr)) if P is None else np.asarray(P, dtype=float)
 
-    l = np.minimum(np.zeros(len(tech_cap)), tech_cap)
+    lb = np.minimum(np.zeros(len(tech_cap)), tech_cap)
     u = np.maximum(tech_cap, np.zeros(len(tech_cap)))
     C, d = _create_C_and_d(tech_cap)
 
-    return ADMMFlexActor(l=l, u=u, C=C, d=d, S=-p_arr)
+    return ADMMFlexActor(lb=lb, u=u, C=C, d=d, S=-p_arr)

distributed_resource_optimization/algorithm/admm/sharing_admm.py

@@ -21,14 +21,12 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any
 
 import cvxpy as cp
 import numpy as np
 
 from .core import ADMMGenericCoordinator, ADMMGlobalActor, ADMMGlobalObjective, ADMMStart
 
-
 # ---------------------------------------------------------------------------
 # Global objective (currently informational only)
 # ---------------------------------------------------------------------------

distributed_resource_optimization/algorithm/consensus/averaging.py

@@ -19,7 +19,7 @@
 
 from __future__ import annotations
 
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Callable
 
 import numpy as np

distributed_resource_optimization/algorithm/heuristic/cohda/core.py

@@ -439,7 +439,8 @@ def decide(
     candidate: SolutionCandidate,
 ) -> tuple[SystemConfig, SolutionCandidate]:
     """Dispatch to the right decide implementation for *decider*."""
-    from .decider import LocalSearchDecider, decide as local_search_decide
+    from .decider import LocalSearchDecider
+    from .decider import decide as local_search_decide
 
     if isinstance(decider, LocalSearchDecider):
         return local_search_decide(cohda_data, decider, sysconfig, candidate)
@@ -564,7 +565,9 @@ def create_cohda_participant(
     """
     if isinstance(schedule_set, list):
         _frozen = [[float(x) for x in s] for s in schedule_set]
-        provider: Callable = lambda _: [np.array(s) for s in _frozen]
+
+        def provider(_: Any, _frozen: list = _frozen) -> list:
+            return [np.array(s) for s in _frozen]
     else:
         provider = schedule_set
 

distributed_resource_optimization/carrier/__init__.py

@@ -1,5 +1,11 @@
 from .core import Carrier, EventWithValue
-from .simple import ActorContainer, SimpleCarrier, cid, start_coordinated_optimization, start_distributed_optimization
+from .simple import (
+    ActorContainer,
+    SimpleCarrier,
+    cid,
+    start_coordinated_optimization,
+    start_distributed_optimization,
+)
 
 __all__ = [
     "Carrier",

distributed_resource_optimization/carrier/mango.py

@@ -54,16 +54,18 @@ async def main():
 from __future__ import annotations
 
 import asyncio
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any
 from uuid import uuid4
 
-from mango import Role, sender_addr as mango_sender_addr
+from mango import Role
+from mango import sender_addr as mango_sender_addr
 
 from .core import Carrier
 
 if TYPE_CHECKING:
     from mango import AgentAddress
+
     from ..algorithm.core import Coordinator, DistributedAlgorithm
 
 

tests/admm/test_consensus_admm.py

@@ -2,14 +2,14 @@
 
 from __future__ import annotations
 
-import pytest
 import numpy as np
+import pytest
 
 from distributed_resource_optimization import (
     ADMMFlexActor,
     create_admm_flex_actor_one_to_many,
-    create_consensus_target_reach_admm_coordinator,
     create_admm_start_consensus,
+    create_consensus_target_reach_admm_coordinator,
     start_coordinated_optimization,
 )
 
@@ -107,7 +107,7 @@ async def test_consensus_admm_complex_four_actors():
     Each actor is at its upper bound, so the solution reproduces the box limits.
     """
     flex_actor1 = ADMMFlexActor(
-        l=[0.0, 0.0, 0.0],
+        lb=[0.0, 0.0, 0.0],
         u=[6.428571428571429, 0.0, 4.5],
         C=np.array([
             [1.0, 1.0, 1.0],
@@ -120,7 +120,7 @@ async def test_consensus_admm_complex_four_actors():
         S=np.array([0.0, 0.0, 0.0]),
     )
     flex_actor2 = ADMMFlexActor(
-        l=[0.0, 0.0, 0.0],
+        lb=[0.0, 0.0, 0.0],
         u=[0.04000000000000001, 0.06, 0.1],
         C=np.array([
             [1.0, 1.0, 1.0],
@@ -133,7 +133,7 @@ async def test_consensus_admm_complex_four_actors():
         S=np.array([0.0, 0.0, 0.0]),
     )
     flex_actor3 = ADMMFlexActor(
-        l=[0.0, 0.0, 0.0],
+        lb=[0.0, 0.0, 0.0],
         u=[0.3, 0.0, 0.3333333333333333],
         C=np.array([
             [1.0, 1.0, 1.0],
@@ -146,7 +146,7 @@ async def test_consensus_admm_complex_four_actors():
         S=np.array([0.0, 0.0, 0.0]),
     )
     flex_actor4 = ADMMFlexActor(
-        l=[0.0, 0.0, 0.0],
+        lb=[0.0, 0.0, 0.0],
         u=[1.5, 0.0, 1.6666666666666665],
         C=np.array([
             [1.0, 1.0, 1.0],