Why Gradient Descent Zigzags and How Momentum Fixes It
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How momentum optimizes gradient descent by dampening oscillations and accelerating convergence on complex The post Why Gradient Descent Zigzags and How Momentum Fixes It appeared first on MarkTechPost .
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News Hub News Hub Premium Content Read our exclusive articles Facebook Instagram X Home Open Source/Weights AI Agents Tutorials Voice AI Robotics Promote with us News Hub Home Open Source/Weights AI Agents Tutorials Voice AI Robotics Promote with us Home Technology Data Science Why Gradient Descent Zigzags and How Momentum Fixes It Technology Data Science Editors Pick Staff Gradient descent has a fundamental limitation: on most real-world loss surfaces, it is inefficient. When the surface has uneven curvature—steep in one direction and flat in another, which is common in practice—the algorithm struggles to make consistent progress. A high learning rate helps move faster along the flat direction but causes overshooting and oscillations along the steep direction. Reducing the learning rate s
Momentum addresses this issue by incorporating information from past gradients. Instead of relying only on the current gradient, it maintains a running average (often called velocity) and updates parameters based on this accumulated direction. As a result, consistent gradients reinforce each other, allowing faster movement across flat regions, while oscillating gradients tend to cancel out, reducing instability.
In this article, we walk through exactly how this works: the update equations, and a from-scratch simulation on a controlled anisotropic surface that lets us measure the difference precisely — 185 steps for vanilla GD versus 159 for Momentum, with β=0.99 failing to converge entirely.
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Key names and topics in this story: Gradient Descent Zigzags, Momentum Fixes It.
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