A Triple-Inertial Accelerated Alternating Optimization Method for Deep Learning Training

• URL: http://arxiv.org/abs/2503.08489v2
• Date: Thu, 13 Mar 2025 12:57:09 GMT
• Title: A Triple-Inertial Accelerated Alternating Optimization Method for Deep Learning Training
• Authors: Chengcheng Yan, Jiawei Xu, Qingsong Wang, Zheng Peng,
• Abstract summary: gradient descent (SGD) algorithm has achieved remarkable success in training deep learning models.<n> alternating minimization (AM) methods have emerged as a promising alternative for model training.<n>We propose a novel Triple-Inertial Accelerated Alternating Minimization (TIAM) framework for neural network training.
• Score: 3.246129789918632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The stochastic gradient descent (SGD) algorithm has achieved remarkable success in training deep learning models. However, it has several limitations, including susceptibility to vanishing gradients, sensitivity to input data, and a lack of robust theoretical guarantees. In recent years, alternating minimization (AM) methods have emerged as a promising alternative for model training by employing gradient-free approaches to iteratively update model parameters. Despite their potential, these methods often exhibit slow convergence rates. To address this challenge, we propose a novel Triple-Inertial Accelerated Alternating Minimization (TIAM) framework for neural network training. The TIAM approach incorporates a triple-inertial acceleration strategy with a specialized approximation method, facilitating targeted acceleration of different terms in each sub-problem optimization. This integration improves the efficiency of convergence, achieving superior performance with fewer iterations. Additionally, we provide a convergence analysis of the TIAM algorithm, including its global convergence properties and convergence rate. Extensive experiments validate the effectiveness of the TIAM method, showing significant improvements in generalization capability and computational efficiency compared to existing approaches, particularly when applied to the rectified linear unit (ReLU) and its variants.

Related papers

• Architect Your Landscape Approach (AYLA) for Optimizations in Deep Learning [0.0]
  Gradient Descent (DSG) and its variants, such as ADAM, are foundational to deep learning optimization. This paper introduces AYLA, a novel optimization technique that enhances adaptability and efficiency rates.
  arXiv  Detail & Related papers  (2025-04-02T16:31:39Z)
• $ψ$DAG: Projected Stochastic Approximation Iteration for DAG Structure Learning [6.612096312467342]
  Learning the structure of Directed A Graphs (DAGs) presents a significant challenge due to the vast search space of possible graphs, which scales with the number of nodes. Recent advancements have redefined this problem as a continuous optimization task by incorporating differentiable a exponentiallyity constraints. We present a novel framework for learning DAGs, employing a Approximation approach integrated with Gradient Descent (SGD)-based optimization techniques.
  arXiv  Detail & Related papers  (2024-10-31T12:13:11Z)
• Towards Differentiable Multilevel Optimization: A Gradient-Based Approach [1.6114012813668932]
  This paper introduces a novel gradient-based approach for multilevel optimization. Our method significantly reduces computational complexity while improving both solution accuracy and convergence speed. To the best of our knowledge, this is one of the first algorithms to provide a general version of implicit differentiation.
  arXiv  Detail & Related papers  (2024-10-15T06:17:59Z)
• A Full Adagrad algorithm with O(Nd) operations [4.389938747401259]
  The study offers efficient and practical algorithms for large-scale applications.<n>This innovative strategy significantly reduces the complexity and resource demands typically associated with full-matrix methods.
  arXiv  Detail & Related papers  (2024-05-03T08:02:08Z)
• Adaptive Federated Learning Over the Air [108.62635460744109]
  We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. Our analysis shows that the AdaGrad-based training algorithm converges to a stationary point at the rate of $mathcalO( ln(T) / T 1 - frac1alpha ).
  arXiv  Detail & Related papers  (2024-03-11T09:10:37Z)
• An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent [10.061799286306163]
  We investigate the convergence behavior of the delta-bar-delta algorithm in real-world neural network optimization. To address any potential convergence challenges, we propose a novel approach called RDBD (Regrettable Delta-Bar-Delta) Our approach allows for prompt correction of biased learning rate adjustments and ensures the convergence of the optimization process.
  arXiv  Detail & Related papers  (2023-10-17T14:15:57Z)
• An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification [97.28167655721766]
  We propose a novel doubly accelerated gradient descent (ADSGD) method for sparsity regularized loss minimization problems. We first prove that ADSGD can achieve a linear convergence rate and lower overall computational complexity.
  arXiv  Detail & Related papers  (2022-08-11T22:27:22Z)
• Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent [20.47598828422897]
  We propose textit-Meta-Regularization, a novel approach for the adaptive choice of the learning rate in first-order descent methods. Our approach modifies the objective function by adding a regularization term, and casts the joint process parameters.
  arXiv  Detail & Related papers  (2021-04-12T13:13:34Z)
• Adaptive Gradient Method with Resilience and Momentum [120.83046824742455]
  We propose an Adaptive Gradient Method with Resilience and Momentum (AdaRem) AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient. Our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error.
  arXiv  Detail & Related papers  (2020-10-21T14:49:00Z)
• Reparameterized Variational Divergence Minimization for Stable Imitation [57.06909373038396]
  We study the extent to which variations in the choice of probabilistic divergence may yield more performant ILO algorithms. We contribute a re parameterization trick for adversarial imitation learning to alleviate the challenges of the promising $f$-divergence minimization framework. Empirically, we demonstrate that our design choices allow for ILO algorithms that outperform baseline approaches and more closely match expert performance in low-dimensional continuous-control tasks.
  arXiv  Detail & Related papers  (2020-06-18T19:04:09Z)
• Cogradient Descent for Bilinear Optimization [124.45816011848096]
  We introduce a Cogradient Descent algorithm (CoGD) to address the bilinear problem. We solve one variable by considering its coupling relationship with the other, leading to a synchronous gradient descent. Our algorithm is applied to solve problems with one variable under the sparsity constraint.
  arXiv  Detail & Related papers  (2020-06-16T13:41:54Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.