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

tests/test_optimizer.cpp

@@ -0,0 +1,42 @@
+#include <gtest/gtest.h>
+#include <torch/torch.h>
+#include "fces/optimizer.hpp"
+
+using namespace fces;
+
+TEST(OptimizerTest, Construction) {
+    auto model = torch::nn::Linear(10, 5);
+    std::vector<torch::Tensor> params;
+    for (auto& p : model->parameters()) params.push_back(p);
+
+    FCESOptimizer opt(params, FCESConfig{}.set_lr(1e-3f));
+    EXPECT_EQ(opt.step_count(), 0);
+}
+
+TEST(OptimizerTest, StepUpdatesCounter) {
+    auto model = torch::nn::Linear(10, 5);
+    std::vector<torch::Tensor> params;
+    for (auto& p : model->parameters()) params.push_back(p);
+
+    FCESOptimizer opt(params, FCESConfig{}.set_lr(1e-3f));
+
+    // Simulate a training step
+    auto x = torch::randn({2, 10});
+    auto y = model->forward(x);
+    auto loss = y.sum();
+    loss.backward();
+    opt.step();
+
+    EXPECT_EQ(opt.step_count(), 1);
+}
+
+TEST(OptimizerTest, UpdateFitness) {
+    auto model = torch::nn::Linear(10, 5);
+    std::vector<torch::Tensor> params;
+    for (auto& p : model->parameters()) params.push_back(p);
+
+    FCESOptimizer opt(params);
+    opt.update_fitness(3.0f);
+    opt.update_fitness(2.5f);
+    // Should not crash
+}