9bbe253810
- CMakeLists.txt with libtorch, GoogleTest, GoogleBenchmark, OpenMP, pybind11 - Header files: config, controller, population, fitness, evolution, spectral, oscillation, telemetry, optimizer - Source implementations: controller (full micro-MLP forward pass, mutation, crossover), fitness (Welford's algorithm), oscillation (DFT), spectral (SVD rank), optimizer (sign-SGD stub) - Tests: controller, population, fitness, optimizer (Google Test) - Benchmarks: evolve throughput, optimizer step (Google Benchmark) - Examples: simple optimization, PyTorch/libtorch integration - Python extension: pybind11 bindings with setup.py - README with architecture diagram and build instructions
37 lines
1.0 KiB
C++
37 lines
1.0 KiB
C++
/**
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* @file simple_optimization.cpp
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* @brief Minimal example: optimize a quadratic function with FCES.
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*/
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#include <iostream>
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#include <torch/torch.h>
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#include "fces/optimizer.hpp"
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int main() {
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// Target: minimize f(x) = ||x - target||^2
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auto target = torch::tensor({1.0f, 2.0f, 3.0f, 4.0f, 5.0f});
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auto x = torch::randn({5}, torch::requires_grad());
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std::vector<torch::Tensor> params = {x};
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fces::FCESOptimizer optimizer(params, fces::FCESConfig{}.set_lr(1e-2f));
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for (int step = 0; step < 500; ++step) {
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optimizer.zero_grad();
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auto loss = (x - target).pow(2).sum();
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loss.backward();
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optimizer.step();
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optimizer.update_fitness(loss.item<float>());
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if (step % 50 == 0) {
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std::cout << "Step " << step
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<< " | Loss: " << loss.item<float>()
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<< " | x: " << x << std::endl;
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}
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}
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std::cout << "\nFinal x: " << x << std::endl;
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std::cout << "Target: " << target << std::endl;
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return 0;
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}
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