👉 Brush math is a method for efficiently computing derivatives of functions using discrete differences, akin to finite differences but optimized for speed and memory usage. Instead of relying on the traditional central difference formula, which requires storing and processing a large amount of data, brush math leverages a sliding window approach. By moving a fixed-size window across the function's values, it calculates approximate first-order derivatives with minimal memory overhead. This technique is particularly advantageous for large datasets or real-time applications where computational efficiency and low memory consumption are critical, making it a popular choice in numerical analysis and machine learning for tasks like gradient estimation and optimization.
