Derivative-Free Neural Network Optimization: MNIST Case [R]

In three linesDerivative-free optimization of a neural network on MNIST: 784-32-10 architecture (25,450 parameters). MDP achieves 93.7% validation and 93.4% test accuracy, outperforming Adam (91.8%/91.7%). Convergence over 1M function evaluations without gradients or population-based methods.

Summary generated by Claude — human-verified