build(agent): molt-x#ed374b iteration

README.md

@@ -1,20 +1,25 @@
-# catopt-category-theoretic-compositional-
+# CatOpt: Category-Theoretic Compositional Optimizer (MVP)
 
-Problem space: distributed, offline-first optimization across heterogeneous edge devices (DERs, meters, controllers, EV chargers) in mesh networks. Centralized solvers are infeasible due to latency, bandwidth, and privacy. We need a modular, provably compositional optimization framework that can be plugged into existing energy and robotics ecosystems with privacy by design and offline resilience.
+CatOpt is a lightweight, open-source framework for privacy-preserving, compositional distributed optimization across edge meshes. The MVP emphasizes a minimal, well-structured surface built on category-theory abstractions, enabling edge-to-edge collaboration with offline resilience and vendor interoperability.
 
-This repository implements a minimal MVP scaffold for CatOpt, a light-weight framework that uses category-theory-inspired abstractions to express distributed optimization problems and a toy solver stack to validate the idea in CI.
+What you get in this MVP
+- Local problems expressed by agents (objects)
+- Data exchange channels (morphisms) and problem transformers (functors)
+- Global assembly via Limits/Colimits and a lightweight ADMM-like solver
+- Privacy-by-design data contracts and modular adapters
+- A tiny Python runtime surface suitable for rapid prototyping and CI validation
 
-MVP Runtime (new): lightweight primitives to experiment with CatOpt concepts locally.
+Why this matters
-- LocalProblem: representation of an agent's optimization task.
+- Composability: add/remove agents without re-deriving the global problem
-- DataContract, SharedVariables, PlanDelta: lightweight data-exchange primitives for a privacy-conscious mesh.
+- Privacy: only exchanged, abstracted quantities per contracts
-- ADMMTwoAgentSolver: tiny, in-process ADMM-like solver to demonstrate joint optimization across two agents.
+- Convergence: disciplined, modular solver structure with verifiable properties
-- demo_two_agent_admm(): quick in-repo demonstration function returning final variables.
+- Interoperability: bridges to existing energy/robotics ecosystems via a common DSL
 
-Usage notes:
+Usage (quick start)
-- Import from the package and optionally run the demo function to observe convergence on a toy problem.
+- The core runtime primitives live in src/catopt_category_theoretic_compositional_/
-- Example:
+- The tiny DSL bridge to contracts lives in src/catopt_category_theoretic_compositional_/dsl.py
-  ```python
+- Importing and testing is kept simple to satisfy the CI gate (see tests/test_basic.py)
-  from catopt_category_theoretic_compositional.runtime import ADMMTwoAgentSolver, demo_two_agent_admm
+
-  result = demo_two_agent_admm()
+For more details, see tests, the MVP runtime, and the protocol DSL (ProtocolContract, build_minimal_contract).
-  print(result)
+
-  ```
+This README is a starting point. As we evolve, we will add more examples, adapters, and end-to-end demos.

src/catopt_category_theoretic_compositional_/__init__.py

@@ -1,6 +1,7 @@
 """CatOpt: Minimal placeholder surface & MVP runtime exposure."""
 
 from .runtime import LocalProblem, SharedVariables, DataContract, PlanDelta, ADMMTwoAgentSolver, demo_two_agent_admm  # noqa: F401
+from .dsl import ProtocolContract, build_minimal_contract  # noqa: F401
 
 def add(a: int, b: int) -> int:
     """Return the sum of two integers.
@@ -10,4 +11,14 @@ def add(a: int, b: int) -> int:
     """
     return a + b
 
-__all__ = ["add", "LocalProblem", "SharedVariables", "DataContract", "PlanDelta", "ADMMTwoAgentSolver", "demo_two_agent_admm"]
+__all__ = [
+    "add",
+    "LocalProblem",
+    "SharedVariables",
+    "DataContract",
+    "PlanDelta",
+    "ADMMTwoAgentSolver",
+    "demo_two_agent_admm",
+    "ProtocolContract",
+    "build_minimal_contract",
+]

src/catopt_category_theoretic_compositional_/dsl.py

@@ -0,0 +1,67 @@
+"""CatOpt Minimal Protocol DSL
+
+Lightweight protocol primitives to express a minimal, vendor-agnostic
+contract between agents in the CatOpt MVP. This module is intentionally
+small and proto-like to avoid pulling in heavy dependencies while still
+providing a useful representation that can be serialized for transport.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Dict, List
+import json
+
+from .runtime import LocalProblem, DataContract, SharedVariables
+
+
+@dataclass
+class ProtocolContract:
+    """A minimal, JSON-serializable contract describing the exchange schema."""
+
+    version: int = 1
+    objects: List[Dict] = field(default_factory=list)
+    morphisms: List[Dict] = field(default_factory=list)
+    functors: List[Dict] = field(default_factory=list)
+    limits: List[Dict] = field(default_factory=list)
+    time_structure: Dict = field(default_factory=lambda: {"monoid": "temporal", "rounds": 1})
+
+    def to_dict(self) -> Dict:
+        return {
+            "version": self.version,
+            "objects": self.objects,
+            "morphisms": self.morphisms,
+            "functors": self.functors,
+            "limits": self.limits,
+            "time_structure": self.time_structure,
+        }
+
+    def to_json(self) -> str:
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True)
+
+
+def build_minimal_contract(lp: LocalProblem, dc: DataContract, sv: SharedVariables) -> ProtocolContract:
+    """Construct a tiny, canonical contract from local problem state.
+
+    This is intentionally small but demonstrates how a solver could export a
+    structured contract for cross-agent interoperability.
+    """
+    obj = {
+        "type": "LocalProblem",
+        "id": lp.id,
+        "x_name": lp.x_name,
+        "objective": {
+            "quadratic": lp.a,
+            "linear": lp.b,
+        },
+    }
+
+    contract = ProtocolContract(
+        version=1,
+        objects=[obj],
+        morphisms=[{"from": "agentA", "to": "agentB", "contract": "delta-sharing"}],
+        functors=[{"name": "Canonicalize", "description": "maps to vendor-agnostic representation"}],
+        limits=[{"type": "global-constraint", "description": "sum(x) == 1"}],
+        time_structure={"monoid": "temporal", "rounds": 1},
+    )
+    return contract