👉 Requires computing is a computational technique used to efficiently solve large-scale optimization problems by breaking them down into smaller, more manageable subproblems that can be solved independently and then combined to find the optimal solution. This approach is particularly useful in machine learning, where it can significantly reduce the computational complexity and time required to train models on massive datasets. By decomposing the problem into smaller tasks, often referred to as "requires," each subproblem can be solved using specialized algorithms, and the solutions are aggregated to obtain the final optimal solution. This method is especially beneficial in scenarios where the problem size is too large to handle directly, allowing for scalable and efficient computation.
