feat: scaffold FCES-native C++ project with libtorch integration

- 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

examples/simple_optimization.cpp

@@ -0,0 +1,36 @@
+/**
+ * @file simple_optimization.cpp
+ * @brief Minimal example: optimize a quadratic function with FCES.
+ */
+
+#include <iostream>
+#include <torch/torch.h>
+#include "fces/optimizer.hpp"
+
+int main() {
+    // Target: minimize f(x) = ||x - target||^2
+    auto target = torch::tensor({1.0f, 2.0f, 3.0f, 4.0f, 5.0f});
+    auto x = torch::randn({5}, torch::requires_grad());
+
+    std::vector<torch::Tensor> params = {x};
+    fces::FCESOptimizer optimizer(params, fces::FCESConfig{}.set_lr(1e-2f));
+
+    for (int step = 0; step < 500; ++step) {
+        optimizer.zero_grad();
+        auto loss = (x - target).pow(2).sum();
+        loss.backward();
+        optimizer.step();
+        optimizer.update_fitness(loss.item<float>());
+
+        if (step % 50 == 0) {
+            std::cout << "Step " << step
+                      << " | Loss: " << loss.item<float>()
+                      << " | x: " << x << std::endl;
+        }
+    }
+
+    std::cout << "\nFinal x: " << x << std::endl;
+    std::cout << "Target:  " << target << std::endl;
+
+    return 0;
+}