Gradient Evaluation

Gradient evaluation is a fundamental computational task in optimization, aiming to efficiently compute the gradient of a function, crucial for finding optimal solutions in various machine learning problems. Current research focuses on developing adaptive and variance-reduced gradient methods, such as stochastic gradient descent variants and accelerated extra-gradient methods, to improve efficiency and robustness, particularly for non-convex and large-scale problems. These advancements are significant because they enable faster and more reliable training of complex models, impacting fields like deep learning and private data analysis where efficient gradient computation is paramount.