梯度下降是一种核心优化算法，在处理不均匀的损失曲面时常常会遇到效率低下的“之字形”收敛问题。这个问题源于曲面的曲率，在一个方向上陡峭而在另一个方向上平坦的特性，导致速度和稳定性之间的权衡。动量是一种结合了过去梯度信息的技术，通过平均方向信息有效地平滑了这些更新。这使得在平坦区域能够更快地前进，同时抑制陡峭方向上的振荡，通过比较显示使用动量所需的步数更少，证明了这一点。 AI
影响 解释了一种对训练大型AI模型至关重要的基本优化技术，可能提高训练效率。
排序理由 技术文章，解释了一种优化算法及其改进，包括数学细节和模拟结果。
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<p>How momentum optimizes gradient descent by dampening oscillations and accelerating convergence on complex</p> <p>The post <a href="https://www.marktechpost.com/2026/05/05/why-gradient-descent-zigzags-and-how-momentum-fixes-it/">Why Gradient Descent Zigzags and How Momentum Fix…
Gradient descent struggles on uneven surfaces - zigzagging instead of converging smoothly. Momentum fixes this by averaging past gradients, dampening oscillations and accelerating convergence. A technical walkthrough shows 185 steps for vanilla GD versus 159 with Momentum. https:…
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