Summary:
- The article discusses methods for computing random gradients, which are important in machine learning and optimization algorithms.
- It covers three main techniques: finite differences, Gaussian random projections, and Hadamard random projections.
- The article provides detailed explanations and comparisons of the performance and computational efficiency of these different methods for estimating random gradients.