CS Professor: 3D Loss Landscape Visualizations Mislead Intuition on Local Minima in High-Dimensional Optimization
CSProfKGD · x · 2026-10-26
A CS professor argues that pedagogically it makes most sense to start with the simple convex case, and that visualizing increasingly messy landscapes in 3D can actually be misleading. He contends this low-dimensional intuition historically fueled skepticism about gradient-based optimization — particularly the fear of getting trapped in poor local minima — because intuitions shaped by 2D/3D landscapes often fail in very high-dimensional problems.
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