Batch Gradient Descent
Batch gradient descent is a foundational optimization algorithm in machine learning, where the entire dataset is used to compute the gradient of the loss function for each iteration. This algorithm adjusts model parameters based on the average error over all training examples, ensuring stable and consistent updates. Introduced in the early 1950s during the development of optimization methods, batch gradient descent is ideal for small to medium-sized datasets where processing the entire dataset in memory is feasible.
https://en.wikipedia.org/wiki/Gradient_descent
The stability of batch gradient descent makes it highly effective in scenarios where precise gradient calculations are crucial. However, the algorithm’s reliance on the full dataset introduces significant computational overhead, particularly for large-scale datasets. This limitation has led to the development of alternative methods like stochastic gradient descent and mini-batch gradient descent, which offer more efficient processing by working on subsets of data. Despite its inefficiency for large data, batch gradient descent remains relevant in linear regression and logistic regression tasks due to its straightforward implementation.
https://en.wikipedia.org/wiki/Stochastic_gradient_descent
Modern machine learning frameworks such as TensorFlow and PyTorch support batch processing and often use batch gradient descent in combination with acceleration techniques like momentum to improve convergence. These implementations benefit from modern GPU and TPU hardware, which allow parallel processing of large datasets]. While batch gradient descent is less commonly used for deep learning due to its inefficiency for large-scale tasks, it continues to serve as a valuable tool in understanding the theoretical aspects of optimization algorithms.
https://pytorch.org/docs/stable/generated/torch.optim.SGD.html
https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD