Memorization vs Greedy Algorithms
Developers should learn and use memorization when working with recursive algorithms or dynamic programming problems where the same subproblems are solved repeatedly, as it can drastically reduce time complexity from exponential to polynomial (e meets developers should learn greedy algorithms for solving optimization problems where speed and simplicity are prioritized, such as in scheduling, graph algorithms (e. Here's our take.
Memorization
Developers should learn and use memorization when working with recursive algorithms or dynamic programming problems where the same subproblems are solved repeatedly, as it can drastically reduce time complexity from exponential to polynomial (e
Memorization
Nice PickDevelopers should learn and use memorization when working with recursive algorithms or dynamic programming problems where the same subproblems are solved repeatedly, as it can drastically reduce time complexity from exponential to polynomial (e
Pros
- +g
- +Related to: dynamic-programming, recursion
Cons
- -Specific tradeoffs depend on your use case
Greedy Algorithms
Developers should learn greedy algorithms for solving optimization problems where speed and simplicity are prioritized, such as in scheduling, graph algorithms (e
Pros
- +g
- +Related to: dynamic-programming, divide-and-conquer
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Memorization if: You want g and can live with specific tradeoffs depend on your use case.
Use Greedy Algorithms if: You prioritize g over what Memorization offers.
Developers should learn and use memorization when working with recursive algorithms or dynamic programming problems where the same subproblems are solved repeatedly, as it can drastically reduce time complexity from exponential to polynomial (e
Disagree with our pick? nice@nicepick.dev