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.
Why Gradient Descent Zigzags and How Momentum Fixes It
MarkTechPost / 5/5/2026
💬 OpinionIdeas & Deep Analysis
Key Points
- The article explains that gradient descent can “zigzag” during optimization when gradients change direction across steps.
- It attributes this behavior to oscillations caused by the curvature of the loss landscape, especially in complex or poorly conditioned problems.
- Momentum is presented as a remedy that dampens oscillations by accumulating a moving average of past gradients.
- By maintaining consistent update direction, momentum can accelerate convergence toward a minimum compared with plain gradient descent.
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