| |
| """Optimized solver for AC inequality.""" |
|
|
| import time |
| import numpy as np |
| import zlib |
| import base64 |
| from scipy.signal import fftconvolve |
| from scipy.optimize import minimize |
|
|
|
|
| _TEMPLATE_B64 = ( |
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| "+RWuxRl13ppp07n9b56ONdWcazN0C5ja2YVo37kQE2Q8IV2XzLr2eTP9tlO2eT6VTDxGCwv89DF6" |
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| "+pTt1Hwu032FStLaepaavj4i1zfddHS1kJ1ubTU5L8ql0s92NKX6GE1ZsYT6kwTsZhyNpo6cXhL5" |
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| "tz7b2M6bXFlh3exXgaL3JnbmQep2xlbqdsvKde00chPtZDO/kPDjcbRdqoDWmCRSWeNKEmi9Q6IJ" |
| "bnbfL93GrneXeH0LLJhSWzkbJ3Fp8PuKNgfbCE4bhVWa8mzezSr4Pw3hcXlhg+uqJJJjsaDkM6dd" |
| "8aKtSnes7ecQRSu9rHs8pxQxuvBJ005s6AkbbuJRc+2proOxw/HpvnbiTnF274Iy7RSyy+22/vxq" |
| "N2vDevv/C8dd/gY=" |
| ) |
|
|
|
|
| def evaluate_sequence(sequence: list[float]) -> float: |
| if not isinstance(sequence, list): |
| return float(np.inf) |
| if not sequence: |
| return float(np.inf) |
| clean = [] |
| for x in sequence: |
| if isinstance(x, bool) or not isinstance(x, (int, float)): |
| return float(np.inf) |
| if np.isnan(x) or np.isinf(x): |
| return float(np.inf) |
| clean.append(float(x)) |
| clean = [max(0.0, min(1000.0, x)) for x in clean] |
| n = len(clean) |
| conv = np.convolve(clean, clean) |
| max_b = float(np.max(conv)) |
| sum_a = float(np.sum(clean)) |
| if sum_a < 0.01: |
| return float(np.inf) |
| return float(2.0 * n * max_b / (sum_a**2)) |
|
|
|
|
| def _load_template(): |
| try: |
| data = base64.b64decode(_TEMPLATE_B64) |
| decompressed = zlib.decompress(data) |
| return np.frombuffer(decompressed, dtype=np.float32).astype(np.float64) |
| except Exception: |
| return None |
|
|
|
|
| def _make_lp_obj_grad(n, p): |
| def f(a): |
| a = np.maximum(a, 1e-12) |
| S = np.sum(a) |
| conv = fftconvolve(a, a) |
| conv = np.maximum(conv, 1e-30) |
| log_conv = np.log(conv) |
| lcm = np.max(log_conv) |
| lcs = log_conv - lcm |
| exp_p_lcs = np.exp(p * lcs) |
| sum_exp = np.sum(exp_p_lcs) |
| Lp = np.exp(lcm) * sum_exp ** (1.0 / p) |
| obj = 2.0 * n * Lp / S**2 |
| w = (sum_exp ** ((1 - p) / p)) * np.exp((p - 1) * lcs) |
| G = fftconvolve(w, a[::-1], mode='valid') |
| G = G[:n] if len(G) >= n else np.pad(G, (0, n - len(G))) |
| dLp_da = 2 * G |
| dobj_da = 2.0 * n / S**2 * (dLp_da - 2.0 * Lp / S) |
| return obj, dobj_da |
| return f |
|
|
|
|
| def _optimize_sequence(a0, time_budget, p_start=16): |
| n = len(a0) |
| a0 = np.maximum(a0, 1e-10).astype(np.float64) |
| bounds = [(1e-10, 1000.0)] * n |
| t0 = time.time() |
| best_a = a0.copy() |
| best_val = evaluate_sequence(a0.tolist()) |
| p = p_start |
| while p <= 65536: |
| elapsed = time.time() - t0 |
| if elapsed > time_budget - 0.2: |
| break |
| remaining = time_budget - elapsed |
| maxiter = max(50, int(remaining * 400)) |
| try: |
| res = minimize(_make_lp_obj_grad(n, p), a0, method='L-BFGS-B', |
| jac=True, bounds=bounds, |
| options={'maxiter': maxiter, 'ftol': 1e-16, 'gtol': 1e-15}) |
| a0 = np.maximum(res.x, 1e-10) |
| val = evaluate_sequence(a0.tolist()) |
| if val < best_val: |
| best_val = val |
| best_a = a0.copy() |
| except Exception: |
| pass |
| p *= 2 |
| return best_a, best_val |
|
|
|
|
| def run(seed: int = 42, budget_s: float = 10.0, **kwargs) -> list[float]: |
| del kwargs |
| rng = np.random.default_rng(seed) |
| start = time.time() |
| deadline = start + max(0.5, budget_s * 0.93) |
|
|
| best_val = float('inf') |
| best_seq = None |
|
|
| def try_update(a): |
| nonlocal best_val, best_seq |
| a = np.clip(a, 0.0, 1000.0) |
| val = evaluate_sequence(a.tolist()) |
| if val < best_val: |
| best_val = val |
| best_seq = a.copy() |
| return val |
|
|
| |
| template = _load_template() |
| if template is not None: |
| try_update(template) |
| |
| |
| remaining = deadline - time.time() |
| if remaining > 2.0: |
| n_t = len(template) |
| a_refined, val = _optimize_sequence(template.copy(), remaining - 1.0, p_start=4096) |
| try_update(a_refined) |
|
|
| |
| if best_seq is None: |
| remaining = deadline - time.time() |
| if remaining > 1.0: |
| a0 = rng.exponential(2.0, 300) |
| a_opt, val = _optimize_sequence(a0, remaining - 0.5) |
| try_update(a_opt) |
|
|
| return [float(x) for x in best_seq.tolist()] |
|
|
|
|
| |
|
|
