Tutorials Data Structures and Algorithms in C#
Dynamic Programming Intro
Dynamic Programming Intro: free step-by-step lesson with examples, common mistakes, and interview tips — part of Data Structures and Algorithms in C# on Toolliyo Academy.
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Data Structures and Algorithms in C# · Lesson 81 of 120
Dynamic Programming Intro
Foundations & Arrays ✓ → Lists, Hash, Trees ✓ → Graphs & DP → Advanced & Projects
Graphs & DP · 3 — Patterns · ~10 min · Dynamic Programming
What is this?
DP solves overlapping subproblems with optimal substructure by storing answers (memo or table) instead of recomputing.
Why should you care?
Once recursion TLEs, DP is the interview upgrade — fib, knapsack, LCS, paths.
See it live — copy this example
Run snippets in a .NET console app, LINQPad, or https://dotnetfiddle.net. Write Big O above every solution.
int Fib(int n, Dictionary<int,int> memo) {
if (n <= 1) return n;
if (memo.TryGetValue(n, out var v)) return v;
return memo[n] = Fib(n - 1, memo) + Fib(n - 2, memo);
}
What happened?
- Memo dictionary caches Fib(n).
- Without it, the recursion tree is exponential.
Practice next
- Compare Fib with and without memo for n=40.
- Write bottom-up fib array.
- Identify overlapping subproblems in coin change.
- Add iterative O(1) space fib.
- Draw the dependency DAG.
Remember
DP = cache subproblems. Define state + transition. Memo or tabulate.
From TLE to AC
Recursive solution times out.
Outcome: You add memo with the right key.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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