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In the last section, we introduced the free monad and implemented it in Lean. In this section we will study the theory a bit more deeply, by understanding the notions of algebra and universality.
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🚧 Under construction 🔐
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If you’ve taken an algorithms class, you have likely seen dynamic programming, specifically a technique called memoization. Memoization works to optimize recursive algorithms by caching the solutions to subproblems in a table, and when a subproblem is encountered, it queries the table instead of recomputing the solution. This gives us an exponential performance boost.
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Free monads provide a way to represent effectful sequential programs as pure syntactic data, separate from their interpretation. You describe what should happen as an abstract tree of effects, leaving open how you want it to happen. By decoupling syntax from semantics like this you gain full control over how programs are evaluated and interpreted - for example we could interpret a syntax tree in multiple ways:
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🚧 Under construction
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🚧 Under construction 🐔
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.