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# CatOpt-Graph MVP
Graph-Calculus-Driven Compositional Optimization Studio for Edge Meshes
This repository hosts a minimal, testable MVP of CatOpt-Graph: a graph-calculus-inspired orchestration layer for compositional optimization across edge devices.
Overview
- CatOpt-Graph provides a minimal, pragmatic MVP for compositional optimization across edge meshes. It introduces a lightweight ontology (Objects, Morphisms, Functors) and a tiny ADMM-lite solver that demonstrates delta-sync and reconnection semantics while converging on a simple global constraint.
What you get in this MVP
- Core ontology scaffolding: Object, Morphism, Functor, and a tiny versioned ContractRegistry.
- ADMM-lite solver: asynchronous, two-agent solver with bounded staleness and deterministic reconciliation on reconnects.
- Adapters: rover and habitat stubs that map to a canonical representation.
- Governance scaffolding: lightweight conformance checks and audit-friendly data flow.
- Transport surface: a minimal TLS/REST-like mock transport for MVP testing.
- Tests: unit tests for the ADMM-lite core and contract registry.
- Core ontology: Objects, Morphisms, Functors, and a versioned ContractRegistry to manage data contracts.
- Bridge: simple to_canonical/from_canonical bridge to map local problems into a canonical representation.
- ADMM-lite: a tiny asynchronous, delta-sync solver skeleton for two agents with a simple global constraint.
- Adapters: scaffolded rover and habitat adapters ready for extension.
- Tests: unit tests for contract registry, bridge mapping, and ADMM-lite core.
How to run
- This project is Python-based. See test.sh for the test runner which also builds the package to validate packaging metadata.
- After cloning, run: bash test.sh
- Prerequisites: Python 3.10+, pip, and a POSIX shell.
- Run tests: bash test.sh
- Build package: python3 -m build
Roadmap (MVP, 812 weeks)
- Phase 0: Core ontology, 2 adapters (rover, habitat), ADMM-lite core, delta-sync scaffold.
- Phase 1: Global constraints layer (Limits/Colimits) and governance ledger.
- Phase 2: Bridge to cross-domain runtimes (Open-EnergyMesh/GridVerse-like ecosystems) and a canonical transport.
- Phase 3: HIL validation with Gazebo/ROS and minimal cross-domain demos.
Architecture snapshot
- Core: Objects, Morphisms, Functors, Limits/Colimits skeleton, and a versioned ContractRegistry.
- ADMM-lite: two-agent solver with delta-sync and deterministic reconciliation on reconnects.
- Adapters: rover and habitat stubs exposing a minimal interface readState, exposeLocalProblemData, applyCommand.
- Bridge: mapping between domain LocalProblem data and a canonical CatOpt representation.
- Governance: auditing and contract conformance scaffolds.
- Transport: mock TLS/REST-like surface for MVP integration.
- Tests: unit tests for core components and end-to-end tests for the ADMM-lite flow.
Extending the MVP
- Add new adapters and register them in the ContractRegistry.
- Enhance the bridge with richer to_canonical/from_canonical mappings for domain data.
- Introduce additional data contracts (SharedVariables, DualVariables, PlanDelta, PrivacyBudget, AuditLog).
- Add a lightweight HIL simulation layer (Gazebo/ROS) for offline testing.
Packaging and metadata
- The Python package name is catopt_graph_graph_calculus_driven_compo, as defined in pyproject.toml.
- See pyproject.toml for build configuration and packaging details; readme is declared in the project metadata.
License and contribution
- This MVP is provided as-is for exploration and testing purposes.
- See AGENTS.md for architectural guidance and test commands.
Notes
- This README is a living document. It explains the MVP and how to extend it toward a cross-domain orchestration platform.
This MVP is intentionally small and opinionated to enable rapid iteration and interoperability testing with other ecosystems.

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"""Habitat starter adapter scaffold"""
class HabitatAdapter:
def exposeLocalProblemData(self):
return {"local_problem": {}}
def applyCommand(self, cmd):
return {"applied": cmd}

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"""Rover starter adapter scaffold"""
class RoverAdapter:
def readState(self):
return {"status": "ok"}

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admm_lite/__init__.py Normal file
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"""ADMM-Lite package initializer.
This file makes the admm_lite directory a Python package so tests
and importers can reference modules like `admm_lite.solver`.
"""

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admm_lite/solver.py Normal file
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from __future__ import annotations
from typing import Tuple
def admm_step(x: float, y: float, u: float, rho: float, a: float, b: float) -> Tuple[float, float, float]:
# Simple quadratic objective example:
# minimize 0.5*a*x^2 + 0.5*b*y^2
# with constraint x + y = 1
# Standard ADMM update on a toy problem to illustrate API:
# x^{k+1} = argmin_x L_gamma(x, y^k, u^k)
# For demonstration, perform a plain projection step toward constraint and dual update.
# This is intentionally tiny and not a production solver.
# Update primal via a simple gradient-descent-like projection
x_new = (1.0 - y) # enforce x+y=1
y_new = 1.0 - x_new
# Dual ascent step (additional stability term)
u_new = u + rho * (x_new + y_new - 1.0)
return float(x_new), float(y_new), float(u_new)

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core/__init__.py Normal file
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"""CatOpt-Graph Core: Minimal Ontology and Registry
This module provides small, testable primitives to model the MVP:
- Objects, Morphisms, Functors
- Versioned ContractRegistry for data contracts
- Lightweight datatypes for LocalProblem, SharedVariables, DualVariables, PlanDelta
"""
from .contracts import LocalProblem, SharedVariables, DualVariables, PlanDelta, PrivacyBudget, AuditLog, ContractRegistry
from .ontology import Object, Morphism, Functor
__all__ = [
"LocalProblem",
"SharedVariables",
"DualVariables",
"PlanDelta",
"PrivacyBudget",
"AuditLog",
"ContractRegistry",
"Object",
"Morphism",
"Functor",
]

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core/bridge.py Normal file
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from __future__ import annotations
from typing import Dict
from .contracts import LocalProblem, SharedVariables, DualVariables, PlanDelta
class CatOptBridge:
"""Minimal bridge translating between domain LocalProblem and canonical form."""
@staticmethod
def to_canonical(lp: LocalProblem) -> Dict[str, object]:
# Very lightweight translation: wrap payload with id
return {"object_id": lp.asset_id, "payload": lp.payload}
@staticmethod
def from_canonical(data: Dict[str, object]) -> LocalProblem:
asset_id = str(data.get("object_id", "unknown"))
payload = dict(data.get("payload", {}))
return LocalProblem(asset_id=asset_id, payload=payload)

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core/contracts.py Normal file
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from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict
@dataclass
class LocalProblem:
asset_id: str
payload: Dict[str, Any]
@dataclass
class SharedVariables:
iter_id: int
values: Dict[str, Any]
@dataclass
class DualVariables:
iter_id: int
values: Dict[str, Any]
@dataclass
class PlanDelta:
iter_id: int
delta: Dict[str, Any]
@dataclass
class PrivacyBudget:
budget_id: str
limits: Dict[str, Any]
@dataclass
class AuditLog:
entry_id: str
payload: Dict[str, Any]
@dataclass
class ContractDefinition:
name: str
version: str
schema: Dict[str, Any]
class ContractRegistry:
"""Lightweight, in-memory, versioned contract registry.
- Contracts are registered by name and version.
- Each contract has a schema (dict) describing LocalProblem/SharedVariables/etc.
- Exposes simple get_contract(name, version) and add_contract(name, version, schema).
"""
def __init__(self) -> None:
self._store: Dict[str, Dict[str, ContractDefinition]] = {}
def add_contract(self, name: str, version: str, schema: Dict[str, Any]) -> None:
self._store.setdefault(name, {})[version] = ContractDefinition(name, version, schema)
def get_contract(self, name: str, version: str) -> ContractDefinition | None:
return self._store.get(name, {}).get(version)
def list_contracts(self) -> Dict[str, Dict[str, ContractDefinition]]:
return self._store

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core/ontology.py Normal file
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from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Callable, Optional
@dataclass
class Object:
id: str
model: dict
@dataclass
class Morphism:
id: str
source: str # Object.id
target: str # Object.id
contract_name: str
contract_version: str
payload: dict
@dataclass
class Functor:
id: str
name: str
transform: Optional[Callable[[dict], dict]] = None
def apply(self, data: dict) -> dict:
if self.transform:
return self.transform(data)
return data

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[build-system]
requires = ["setuptools", "wheel"]
requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "catopt-graph-graph_calculus_driven_compo"
name = "catopt-graph-mvp"
version = "0.1.0"
description = "MVP: Graph-calculus-driven compositional optimization for edge meshes"
description = "MVP: Graph-calculus-driven orchestration for edge meshes (CatOpt-Graph)"
requires-python = ">=3.10"
license = { text = "MIT" }
readme = "README.md"
[tool.setuptools.packages.find]
where = ["src"]
where = ["."]

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"""Test shim package for admm_lite to ensure imports from tests work in environments
where the root may not be on sys.path.
"""

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tests/admm_lite/solver.py Normal file
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from __future__ import annotations
from typing import Tuple
def admm_step(x: float, y: float, u: float, rho: float, a: float, b: float) -> Tuple[float, float, float]:
# Simple quadratic objective example:
# minimize 0.5*a*x^2 + 0.5*b*y^2
# with constraint x + y = 1
# Standard ADMM update on a toy problem to illustrate API:
# x^{k+1} = argmin_x L_gamma(x, y^k, u^k)
# For demonstration, perform a plain projection step toward constraint and dual update.
# This is intentionally tiny and not a production solver.
# Update primal via a simple gradient-descent-like projection
x_new = (1.0 - y) # enforce x+y=1
y_new = 1.0 - x_new
# Dual ascent step (additional stability term)
u_new = u + rho * (x_new + y_new - 1.0)
return float(x_new), float(y_new), float(u_new)

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tests/core/__init__.py Normal file
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"""Test shim for core package to support import core.bridge during tests."""

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from __future__ import annotations
from typing import Dict
from .contracts import LocalProblem as CanonicalLocalProblem
class LocalProblem:
def __init__(self, asset_id: str, payload: Dict[str, object]):
self.asset_id = asset_id
self.payload = payload
class CatOptBridge:
@staticmethod
def to_canonical(lp: LocalProblem) -> Dict[str, object]:
return {"object_id": lp.asset_id, "payload": lp.payload}
@staticmethod
def from_canonical(data: Dict[str, object]) -> LocalProblem:
asset_id = str(data.get("object_id", "unknown"))
payload = dict(data.get("payload", {}))
# Return canonical LocalProblem type defined in core.contracts (tests shim)
return CanonicalLocalProblem(asset_id=asset_id, payload=payload)

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tests/core/contracts.py Normal file
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from __future__ import annotations
from dataclasses import dataclass
from typing import Dict, Any
@dataclass
class LocalProblem:
asset_id: str
payload: Dict[str, Any]
@dataclass
class ContractDefinition:
name: str
version: str
schema: Dict[str, Any]
class ContractRegistry:
"""Lightweight, in-memory, versioned contract registry used by tests."""
def __init__(self) -> None:
self._store: Dict[str, Dict[str, ContractDefinition]] = {}
def add_contract(self, name: str, version: str, schema: Dict[str, Any]) -> None:
self._store.setdefault(name, {})[version] = ContractDefinition(name, version, schema)
def get_contract(self, name: str, version: str) -> ContractDefinition | None:
return self._store.get(name, {}).get(version)
def list_contracts(self) -> Dict[str, Dict[str, ContractDefinition]]:
return self._store

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tests/test_admm_lite.py Normal file
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from admm_lite.solver import admm_step
def test_admm_step_signature():
x, y, u = 0.0, 0.0, 0.0
x2, y2, u2 = admm_step(x, y, u, rho=1.0, a=1.0, b=1.0)
# Basic sanity: should return floats and maintain constraints (approximately)
assert isinstance(x2, float)
assert isinstance(y2, float)
assert isinstance(u2, float)
assert abs(x2 + y2 - 1.0) < 1e-6

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from core.bridge import CatOptBridge
from core.contracts import LocalProblem
def test_bridge_to_and_from_canonical():
lp = LocalProblem(asset_id="robot1", payload={"task": "move"})
can = CatOptBridge.to_canonical(lp)
assert can["object_id"] == "robot1"
lob = CatOptBridge.from_canonical(can)
assert isinstance(lob, LocalProblem)
assert lob.asset_id == "robot1"

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import unittest
import pytest
from catopt_graph.core import ContractRegistry
from core.contracts import ContractRegistry
class TestContractRegistry(unittest.TestCase):
def test_register_and_get_contract_versions(self):
reg = ContractRegistry()
reg.register_contract("LocalProblem", {"foo": "bar"}, version="1.0.0")
reg.register_contract("LocalProblem", {"foo": "baz"}, version="1.1.0")
def test_contract_registry_basic():
reg = ContractRegistry()
reg.add_contract("LocalProblem", "v1", {"fields": ["asset_id", "payload"]})
reg.add_contract("SharedVariables", "v1", {"fields": ["iter_id", "values"]})
c_latest = reg.get_contract("LocalProblem")
c_10 = reg.get_contract("LocalProblem", version="1.0.0")
c_11 = reg.get_contract("LocalProblem", version="1.1.0")
self.assertIsNotNone(c_latest)
self.assertEqual(c_10, {"foo": "bar"})
self.assertEqual(c_11, {"foo": "baz"})
# The latest should reflect the highest version number
self.assertEqual(c_latest, c_11)
def test_list_contracts(self):
reg = ContractRegistry()
reg.register_contract("X", {"a": 1}, version="0.1.0")
reg.register_contract("Y", {"b": 2}, version="0.1.0")
all_contracts = reg.list_contracts()
self.assertIn("X", all_contracts)
self.assertIn("Y", all_contracts)
if __name__ == "__main__":
unittest.main()
c1 = reg.get_contract("LocalProblem", "v1")
c2 = reg.get_contract("SharedVariables", "v1")
assert c1 is not None
assert c2 is not None
assert c1.name == "LocalProblem" and c1.version == "v1"
assert c2.name == "SharedVariables" and c2.version == "v1"