ccevolve/tx_workspace/ac1_alphaevolve/solution.py

import jax
import jax.numpy as jnp
import optax
import numpy as np
from dataclasses import dataclass


@dataclass
class Hyperparameters:
    num_intervals: int = 1000
    learning_rate: float = 0.01
    end_lr_factor: float = 1e-5
    num_steps: int = 80000
    warmup_steps: int = 3000
    smooth_max_temp: float = 100.0  # temperature for log-sum-exp smooth max


class AutocorrelationOptimizer:
    def __init__(self, hypers: Hyperparameters):
        self.hypers = hypers
        self.domain_width = 0.5
        self.dx = self.domain_width / self.hypers.num_intervals

    def _objective_fn(self, f_values: jnp.ndarray, temp: float) -> jnp.ndarray:
        f_non_negative = jax.nn.softplus(f_values)  # smooth non-negativity
        integral_f = jnp.sum(f_non_negative) * self.dx
        eps = 1e-12
        integral_f_safe = jnp.maximum(integral_f, eps)

        N = self.hypers.num_intervals
        padded_f = jnp.pad(f_non_negative, (0, N))
        fft_f = jnp.fft.rfft(padded_f)
        fft_conv = fft_f * fft_f
        conv_f_f = jnp.fft.irfft(fft_conv, n=2 * N)

        scaled_conv_f_f = conv_f_f * self.dx

        # Use log-sum-exp smooth max for better gradients
        smooth_max = jax.nn.logsumexp(temp * scaled_conv_f_f) / temp

        c1_ratio = smooth_max / (integral_f_safe ** 2)
        return c1_ratio

    def _hard_objective(self, f_values: jnp.ndarray) -> jnp.ndarray:
        f_non_negative = jax.nn.softplus(f_values)
        integral_f = jnp.sum(f_non_negative) * self.dx
        eps = 1e-12
        integral_f_safe = jnp.maximum(integral_f, eps)

        N = self.hypers.num_intervals
        padded_f = jnp.pad(f_non_negative, (0, N))
        fft_f = jnp.fft.rfft(padded_f)
        fft_conv = fft_f * fft_f
        conv_f_f = jnp.fft.irfft(fft_conv, n=2 * N)
        scaled_conv_f_f = conv_f_f * self.dx
        max_conv = jnp.max(scaled_conv_f_f)
        c1_ratio = max_conv / (integral_f_safe ** 2)
        return c1_ratio

    def train_step(self, f_values, opt_state, temp):
        loss, grads = jax.value_and_grad(self._objective_fn)(f_values, temp)
        updates, opt_state = self.optimizer.update(grads, opt_state, f_values)
        f_values = optax.apply_updates(f_values, updates)
        hard_loss = self._hard_objective(f_values)
        return f_values, opt_state, loss, hard_loss

    def run_optimization(self, seed=42):
        schedule = optax.warmup_cosine_decay_schedule(
            init_value=0.0,
            peak_value=self.hypers.learning_rate,
            warmup_steps=self.hypers.warmup_steps,
            decay_steps=self.hypers.num_steps - self.hypers.warmup_steps,
            end_value=self.hypers.learning_rate * self.hypers.end_lr_factor,
        )
        self.optimizer = optax.adam(learning_rate=schedule)

        key = jax.random.PRNGKey(seed)
        N = self.hypers.num_intervals

        # Better initialization: triangle-like shape centered
        x = jnp.linspace(0, 1, N)
        # Start with a bump function shape
        f_values = jnp.exp(-20.0 * (x - 0.5) ** 2)
        f_values = f_values + 0.05 * jax.random.uniform(key, (N,))

        opt_state = self.optimizer.init(f_values)

        train_step_jit = jax.jit(self.train_step)

        best_loss = jnp.inf
        best_f = f_values

        for step in range(self.hypers.num_steps):
            # Anneal temperature: start low, increase
            progress = min(step / (self.hypers.num_steps * 0.5), 1.0)
            temp = 10.0 + progress * (self.hypers.smooth_max_temp - 10.0)

            f_values, opt_state, loss, hard_loss = train_step_jit(f_values, opt_state, temp)

            if hard_loss < best_loss:
                best_loss = hard_loss
                best_f = f_values

            if step % 5000 == 0 or step == self.hypers.num_steps - 1:
                print(f"Step {step:5d} | C1(smooth) ≈ {loss:.8f} | C1(hard) ≈ {hard_loss:.8f} | temp={temp:.1f}")

        print(f"Best C1 found: {best_loss:.8f}")

        # Convert softplus to actual non-negative values
        final_f = jax.nn.softplus(best_f)
        final_c1 = float(best_loss)
        return final_f, final_c1


def run():
    best_c1 = float('inf')
    best_result = None

    # Try multiple seeds and configs
    configs = [
        (1000, 0.01, 80000, 42),
        (1000, 0.005, 80000, 123),
        (1500, 0.008, 60000, 42),
    ]

    for n_intervals, lr, steps, seed in configs:
        print(f"\n--- Config: N={n_intervals}, lr={lr}, steps={steps}, seed={seed} ---")
        hypers = Hyperparameters(
            num_intervals=n_intervals,
            learning_rate=lr,
            num_steps=steps,
        )
        optimizer = AutocorrelationOptimizer(hypers)
        optimized_f, final_c1 = optimizer.run_optimization(seed=seed)

        if final_c1 < best_c1:
            best_c1 = final_c1
            f_values_np = np.array(optimized_f)
            best_result = (f_values_np, best_c1, best_c1, hypers.num_intervals)
            print(f"*** New best: C1 = {best_c1:.10f}")

    return best_result