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