build(agent): new-agents#a6e6ec iteration

.gitignore

@@ -0,0 +1,21 @@
+node_modules/
+.npmrc
+.env
+.env.*
+__tests__/
+coverage/
+.nyc_output/
+dist/
+build/
+.cache/
+*.log
+.DS_Store
+tmp/
+.tmp/
+__pycache__/
+*.pyc
+.venv/
+venv/
+*.egg-info/
+.pytest_cache/
+READY_TO_PUBLISH

AGENTS.md

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+# AGENTS.md
+
+Architecture and contribution guide for CatOpt-Grid.
+
+- Goal: provide a production-ready, cross-domain, privacy-preserving distributed optimization framework inspired by category theory.
+- Scope: MVP skeleton that establishes core primitives (Objects, Morphisms, Functors) and a minimal ADMM-like solver with delta-sync semantics.
+- Roles: agents (local problem solvers), adapters (Cross-domain mappings), channels (data exchange), and governance/logging components.
+
+Project Rules
+- Follow the architecture described in the main repository README and this AGENTS.md.
+- Tests must pass locally via the provided test.sh script.
+- Packaging must be verifiable by python3 -m build and the project must expose a valid packaging entry (pyproject.toml).
+- Do not introduce breaking changes to the public API without updating tests and documentation.
+
+Development workflow
+- Implement small, well-scoped changes first (per the editing approach).
+- Write/adjust tests to cover new behavior.
+- Update README with usage, install, and contribution guidelines.
+
+Testing and CI
+- Run test.sh locally to validate functionality and packaging viability.
+- Ensure test coverage exercises core primitives: LocalProblem, SharedVariable, and the ADMM-lite solver.
+
+Changelog and governance
+- Document design decisions, tradeoffs, and privacy/security considerations in the README and AGENTS.md.
+
+End of AGENTS.md

README.md

@@ -1,4 +1,22 @@
-# catopt-grid-category-theoretic-compositi
+# CatOpt-Grid
 
-A modular, open-source framework that expresses distributed optimization problems across heterogeneous edge devices (DERs, meters, mobility chargers, water pumps) in a category-theory-inspired formalism. CatOpt-Grid defines a small calculus where:
+Category-Theoretic Compositional Optimizer for Cross-Domain, Privacy-Preserving Distributed Edge Meshes.
-- 
+
+This repository provides a production-ready skeleton for CatOpt-Grid, a framework that expresses distributed optimization problems across heterogeneous edge devices (DERs, meters, mobility chargers, water pumps) using category-theoretic primitives: Objects (local problems), Morphisms (data exchange channels), and Functors (adapters). It aims to enable composability, privacy-preserving aggregation, and delta-sync semantics across partitions.
+
+Design goals
+- Privacy by design: secure aggregation, optional local differential privacy, and federated updates.
+- Distributed optimization core: a robust, ADMM-like solver with convergence guarantees for broad convex classes.
+- Cross-domain adapters: marketplace and SDK; codegen targets (Rust/C) for edge devices; schema registry for interoperability.
+- Governance and data policy: auditable logs and policy fragments for visibility control.
+- Open interoperability: plasma with Open-EnergyMesh and CosmosMesh for cross-domain coordination.
+
+Getting started
+- This is a skeleton MVP focused on core primitives and a minimal solver to enable testing and integration.
+- Install: python3 -m pip install . (after packaging)
+- Run tests: bash test.sh
+
+Contributing
+- See AGENTS.md for architectural rules and contribution guidelines.
+
+READY_TO_PUBLISH marker is used to signal completion in the publishing workflow.

catopt_grid/__init__.py

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+from .core import LocalProblem, SharedVariable, PlanDelta, TimeRound
+from .solver import admm_lite
+
+__all__ = ["LocalProblem", "SharedVariable", "PlanDelta", "TimeRound", "admm_lite"]

catopt_grid/core.py

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+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, List, Optional
+
+
+
+@dataclass
+class TimeRound:
+    index: int
+    timestamp: float
+
+
+@dataclass
+class PlanDelta:
+    delta: List[float]
+    version: int
+
+
+@dataclass
+class SharedVariable:
+    name: str
+    value: List[float]
+    version: int = 0
+
+
+@dataclass
+class LocalProblem:
+    id: str
+    dimension: int
+    # Optional gradient function for the local objective: grad(x) -> list of length dimension
+    objective_grad: Optional[Callable[[List[float]], List[float]]] = None
+    # Optional per-asset offset that the gradient may push towards
+    target: Optional[List[float]] = None
+    data: Optional[dict] = None
+
+    def __post_init__(self):
+        if self.dimension <= 0:
+            raise ValueError("dimension must be positive")
+        if self.target is not None and len(self.target) != self.dimension:
+            raise ValueError("target shape must match dimension")

catopt_grid/solver.py

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+from __future__ import annotations
+
+from typing import List, Dict
+
+from .core import LocalProblem
+
+
+def admm_lite(problems: List[LocalProblem], rho: float = 1.0, max_iter: int = 50) -> Dict:
+    """
+    A minimal ADMM-lite solver across a set of LocalProblem instances.
+
+    This is a toy implementation intended for integration testing and as a
+    scaffold for real solvers. Each problem provides a gradient function
+    objective_grad(x). If not provided, the gradient is assumed to be zero.
+
+    The solver maintains local variables x_i for each problem and a consensus
+    variable z. It performs a simple primal update followed by a consensus step.
+    The function returns a dict containing the final local variables and the consensus.
+    """
+
+    if len(problems) == 0:
+        return {"X": [], "Z": None, "iterations": 0}
+
+    dims = [p.dimension for p in problems]
+    if not all(d == dims[0] for d in dims):
+        raise ValueError("All problems must have the same dimension for this toy solver.")
+
+    dim = dims[0]
+    # Initialize local variables and consensus as Python lists
+    X: List[List[float]] = [[0.0 for _ in range(dim)] for _ in problems]
+    Z: List[float] = [0.0 for _ in range(dim)]
+
+    def _grad(p: LocalProblem, x: List[float]) -> List[float]:
+        if p.objective_grad is None:
+            return [0.0 for _ in range(dim)]
+        return p.objective_grad(x)
+
+    for _ in range(max_iter):
+        # Local update (proximal-like step towards consensus Z)
+        for i, p in enumerate(problems):
+            g = _grad(p, X[i])
+            # X[i] = X[i] - (1/rho) * g - (1/rho) * (X[i] - Z)
+            for d in range(dim):
+                X[i][d] = X[i][d] - (1.0 / max(1e-8, rho)) * g[d] - (1.0 / max(1e-8, rho)) * (X[i][d] - Z[d])
+
+        # Global consensus update (element-wise average)
+        for d in range(dim):
+            Z[d] = sum(X[i][d] for i in range(len(X))) / len(X)
+
+    return {"X": X, "Z": Z, "iterations": max_iter}

pyproject.toml

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+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "catopt-grid"
+version = "0.1.0"
+description = "Category-theoretic compositional optimizer skeleton for cross-domain distributed edge meshes (MVP)."
+readme = "README.md"
+requires-python = ">=3.9"
+license = {text = "MIT"}
+authors = [{name = "OpenCode AI", email = "ops@example.com"}]
+dependencies = [
+  "numpy>=1.23",
+  "pydantic>=1.10",
+]
+
+[tool.setuptools.packages.find]
+where = ["catopt_grid"]

test.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+echo "Running tests..."
+pytest -q
+echo "Building package..."
+python3 -m build
+echo "All tests passed."

tests/test_catopt_grid.py

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+import sys
+import os
+repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+if repo_root not in sys.path:
+    sys.path.insert(0, repo_root)
+
+from catopt_grid.core import LocalProblem
+from catopt_grid.solver import admm_lite
+
+
+def test_admm_lite_basic_convergence_like_setup():
+    # Two problems with dimension 2
+    def grad_factory(target):
+        def g(x):
+            return [xi - ti for xi, ti in zip(x, target)]
+        return g
+
+    p1 = LocalProblem(id="p1", dimension=2, objective_grad=grad_factory([1.0, 0.0]))
+    p2 = LocalProblem(id="p2", dimension=2, objective_grad=grad_factory([0.0, 1.0]))
+
+    res = admm_lite([p1, p2], rho=1.0, max_iter=20)
+
+    X = res["X"]
+    Z = res["Z"]
+
+    assert len(X) == 2
+    for xi in X:
+        assert len(xi) == 2
+        assert all(not (val != val) for val in xi)  # no NaN
+    assert len(Z) == 2
+    # In this toy setup, the consensus should lie between the two targets [1,0] and [0,1]
+    target_mean = [0.5, 0.5]
+    assert all(abs(a - b) <= 0.5 for a, b in zip(Z, target_mean))