Proximal gradient methods for learning

Why can't we always find the best path in a maze?

Imagine you're trying to find the shortest route through a city with many one-way streets. You can't just follow the streets; you need a smarter way to navigate.

Proximal gradient descent helps us find the best path by breaking down the problem into smaller, easier steps, even when the road map isn't smooth.

Example

Instead of trying to find the shortest route in one go, you first follow the streets (simple steps), then adjust your path (gradually improving) until you reach your destination.

Remember this

Proximal gradient descent allows us to tackle complex problems by simplifying them into manageable steps.

Text adapted from Wikipedia, licensed under CC BY-SA 4.0.

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