Efficient learning rate adaptation based on hierarchical optimization approach.

Area of Science:

• Machine Learning
• Deep Learning Optimization

Background:

• Gradient-based optimization methods are fundamental to deep learning.
• Effective learning rate scheduling is crucial for model convergence and performance.
• Existing adaptive learning rate methods often require complex tuning or additional hyperparameters.

Purpose of the Study:

• To propose a new hierarchical approach for learning rate adaptation called learning rate optimization (LRO).
• To develop an efficient and effective method for optimizing learning rates in gradient descent algorithms.
• To demonstrate the superiority of LRO integrated with existing optimizers over state-of-the-art methods.

Main Methods:

• Formulating learning rate adaptation as a hierarchical optimization problem.
• Minimizing the loss function with respect to the learning rate for current model parameters and gradients.
• Utilizing the alternating direction method of multipliers (ADMM) for learning rate optimization.
• Avoiding the need for second-order information or probabilistic models, ensuring high efficiency.

Main Results:

• LRO demonstrated high efficiency by not requiring second-order information or probabilistic models.
• The proposed method does not introduce additional hyperparameters compared to standard exponential decay.
• Integration of LRO with SGD and Adam significantly improved optimization performance.
• SGD and Adam with LRO outperformed all competing methods on image classification benchmarks.

Conclusions:

• LRO offers an efficient and effective approach to learning rate adaptation in gradient-based methods.
• The proposed method provides substantial performance gains in image classification tasks.
• LRO represents a promising advancement for optimizing deep learning models.