build(agent): new-agents-2#7e3bbc iteration

catopt_studio/__init__.py

@@ -1,6 +1,7 @@
 """Public API for the CatOpt Studio MVP."""
 
 from .core import LocalProblem, SharedVariables, PlanDelta, PrivacyBudget, AuditLog, PolicyBlock, GraphOfContractsEntry
+from .solver import SolverConfig, compile_to_solver, simulate_solver_step
 
 __all__ = [
     "LocalProblem",
@@ -10,4 +11,7 @@ __all__ = [
     "AuditLog",
     "PolicyBlock",
     "GraphOfContractsEntry",
+    "SolverConfig",
+    "compile_to_solver",
+    "simulate_solver_step",
 ]

catopt_studio/solver.py

@@ -0,0 +1,92 @@
+"""Minimal solver compiler for CatOpt Studio MVP.
+
+This module provides a tiny, production-ready stub that translates the DSL
+primitives (LocalProblem, SharedVariables) into a lightweight solver
+configuration suitable for an ADMM-like, edge-friendly solver backend.
+
+The goal here is to offer a stable, testable bridge between the DSL surface
+and a real solver, without pulling in heavy dependencies or implementing a
+full solver stack.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+from .core import LocalProblem, SharedVariables
+
+
+@dataclass
+class SolverConfig:
+    """Lightweight representation of a solver configuration.
+
+    This is intentionally minimal and intended to be consumed by a real
+    solver backend later. It captures a solver type and a few hyperparameters
+    used by an ADMM-like routine.
+    """
+
+    solver_type: str
+    rho: float
+    max_iterations: int
+    tolerance: float
+    delta_sync: bool = False
+
+
+def compile_to_solver(lp: LocalProblem, sv: SharedVariables, *, phase: int = 0) -> SolverConfig:
+    """Translate a DSL LocalProblem into a SolverConfig.
+
+    This is a lightweight heuristic intended for MVP demonstrations. The
+    mapping is intentionally simple and deterministic to support deterministic
+    replay during testing and islanding scenarios.
+    """
+
+    obj = (lp.objective or "").lower()
+    domain = (lp.domain or "").lower()
+
+    # Pick a solver flavor based on domain/objective hints
+    if "energy" in domain:
+        solver_type = "admm-lite-energy"
+    elif "cost" in obj or "min" in obj:
+        solver_type = "admm-lite-cost"
+    else:
+        solver_type = "admm-lite"
+
+    # Simple heuristics for hyperparameters; keep things small and safe for
+    # edge devices in MVP mode.
+    rho = 1.0 if "energy" in domain else 0.5
+    max_iterations = 50
+    tolerance = 1e-4
+
+    return SolverConfig(
+        solver_type=solver_type,
+        rho=float(rho),
+        max_iterations=int(max_iterations),
+        tolerance=float(tolerance),
+        delta_sync=(phase > 0),
+    )
+
+
+def simulate_solver_step(
+    config: SolverConfig, lp: LocalProblem, sv: SharedVariables, delta: Optional[Dict[str, Any]] = None
+) -> Dict[str, Any]:
+    """Emit a minimal, deterministic solver step result.
+
+    This is a small helper to illustrate how a step might evolve given a
+    configuration and current problem/variables. It does not perform any real
+    optimization; it merely promotes structure for tests and demos.
+    """
+
+    # Produce a tiny, deterministic artifact that mirrors what a real step might
+    # include (iteration index, delta, residual proxy).
+    result: Dict[str, Any] = {
+        "iteration": 1,
+        "config": {
+            "solver_type": config.solver_type,
+            "rho": config.rho,
+            "max_iterations": config.max_iterations,
+            "tolerance": config.tolerance,
+        },
+        "delta_applied": delta or {},
+        "primal_residual": max(0.0, 1.0 - float(config.rho)),
+    }
+    return result

tests/test_solver.py

@@ -0,0 +1,26 @@
+import datetime
+
+import pytest
+
+from catopt_studio import LocalProblem, SharedVariables
+from catopt_studio.solver import compile_to_solver, simulate_solver_step, SolverConfig
+
+
+def test_compile_to_solver_basic():
+    lp = LocalProblem(id="lp1", domain="energy", assets={"battery": 100}, objective="min_cost")
+    sv = SharedVariables()
+    sc = compile_to_solver(lp, sv)
+    assert isinstance(sc, SolverConfig)
+    assert sc.solver_type in ("admm-lite-energy", "admm-lite-cost", "admm-lite")
+    assert sc.max_iterations == 50
+    assert sc.rho > 0
+
+
+def test_simulate_solver_step_returns_dict():
+    lp = LocalProblem(id="lp1", domain="energy", assets={}, objective="min_cost")
+    sv = SharedVariables()
+    sc = compile_to_solver(lp, sv)
+    step = simulate_solver_step(sc, lp, sv, delta={"x": 1})
+    assert isinstance(step, dict)
+    assert step.get("iteration") == 1
+    assert step.get("delta_applied") == {"x": 1}