Saturday, 24 October 2026 · Weekend classic from 2014 · 5 min read
Adam optimizer adapts learning rates using moment estimates for efficient stochastic optimization
Adam is a first-order stochastic optimization algorithm that uses adaptive estimates of lower-order moments of gradients, offering computational efficiency, low memory use, and robustness to noisy or sparse gradients, with strong theoretical convergence guarantees and practical performance.
Based on the paper Adam: A Method for Stochastic Optimization by Diederik P. Kingma, Jimmy Ba
Challenges in stochastic optimization for large-scale problems
Optimizing stochastic objective functions, especially in machine learning, involves handling noisy and possibly sparse gradients that vary over time. Traditional gradient descent methods use fixed or manually tuned learning rates, which can be inefficient or unstable when gradients have different scales or when the objective is non-stationary.
Large-scale problems with many parameters and large datasets require optimization algorithms that are computationally efficient and have low memory overhead. Additionally, the optimization method should be robust to gradient noise and adapt to changing conditions during training.
Previous adaptive methods have been proposed, but they often lack a combination of computational efficiency, theoretical convergence guarantees, and practical robustness to noisy or sparse gradients. This motivates the development of a new optimizer that addresses these challenges effectively.
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