DP vs Recursion vs Greedy Choosing the Right Tool in DSA Typescript - Key Differences

Recall & Review

beginner

What is recursion in problem solving?

Recursion is a method where a function calls itself to solve smaller parts of a problem until it reaches a simple base case.

intermediate

What does DP (Dynamic Programming) do differently from simple recursion?

DP stores results of solved subproblems to avoid repeating work, making it faster than simple recursion for problems with overlapping subproblems.

beginner

When is a greedy algorithm the right choice?

A greedy algorithm works best when making the best local choice at each step leads to the best overall solution, without needing to reconsider previous choices.

intermediate

What kind of problems are best solved by DP rather than greedy?

Problems with overlapping subproblems and optimal substructure, where local choices alone don’t guarantee the best global solution, are best solved by DP.

intermediate

How can you decide between recursion, DP, and greedy for a new problem?

Check if the problem has overlapping subproblems and optimal substructure (use DP), if local best choices lead to global best (use greedy), or if it’s simple and small (recursion may be enough).

Which approach stores results to avoid repeated work?

A Brute Force

B Simple Recursion

C Dynamic Programming

D Greedy Algorithm

Dynamic Programming saves results of subproblems to avoid recalculating them.

When is a greedy algorithm NOT suitable?

A When problem has no overlapping subproblems

B When local choices don’t guarantee global best

C When problem is very small

D When recursion is easy to implement

Greedy fails if local best choices don’t lead to the best overall solution.

What is a base case in recursion?

A The simplest case that stops recursion

B The largest input size

C A repeated subproblem

D A greedy choice

Base case stops recursion by providing a direct answer.

Which property means a problem can be broken into smaller similar problems?

A Base case

B Greedy choice property

C Recursion depth

D Optimal substructure

Optimal substructure means the best solution can be built from best solutions of subproblems.

What is overlapping subproblems in DP?

A When the same subproblems appear multiple times

B When subproblems are all different

C When greedy choices overlap

D When recursion never ends

Overlapping subproblems means the same smaller problems repeat, so storing results helps.

Explain how to choose between recursion, dynamic programming, and greedy algorithms for solving a problem.

Think about problem structure and if repeated work happens.

Describe the main difference between dynamic programming and greedy algorithms.

Focus on how each approach makes decisions.