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ActiveLLM Project

DP Visualizer

Deconstruct Dynamic Programming. Compare top-down memoized recursion trees with bottom-up tabulation tables step-by-step.

Target Learning Outcome

Optimize exponential recursive functions using top-down memoization caches and bottom-up tabulation arrays.

Active Lab TerminalRUNNING LIVE
Top-Down Recursion Tree
F(5)
Calculated
F(4)
Pruned (Memoized)
F(3)

💡 **Notice**: The orange colored sub-branches are pruned immediately because the values are already computed and loaded from the memoization lookup cache, resulting in **O(N)** time instead of **O(2^N)**.

Bottom-Up Tabulation Array
dp[0]
0
dp[1]
1
dp[2]
1
dp[3]
2
dp[4]
3
dp[5]
5
Select Target (N)

* Click Tabulate to trigger bottom-up array steps, filling elements iteratively from index 0.

Execution logs
Select a DP problem and trigger step animations.
01. Problem it solves

Exponential Subproblem Explosion

Dynamic programming is notoriously difficult because it requires visualizing overlapping calculations. Understanding how memoization saves steps, or how a bottom-up array aggregates previous states, is rarely visible to learners, leading to pure code memorization.

This visualizer displays the recursion tree pruning and tabulation grid filling side-by-side, making the space-time optimization steps crystal clear.

02. Concept it teaches

Memoization Caching and Tabulation Grids

This sandbox teaches core dynamic programming concepts:

  • Overlapping subproblems identification
  • Optimal substructure choices (take/skip)
  • Top-Down memoization recursion tree caching
  • Bottom-Up tabulation iterative array filling
  • Time-space tradeoffs (O(2^N) reduced to O(N))
03. What I built

Dual-panel DP Workspace

An interactive dynamic programming sandbox featuring:

  • Interactive tab switcher to select: Fibonacci, Climbing Stairs, or Coin Change.
  • Target input N values adjustments recalculating grids dynamically.
  • Split comparison view between top-down recursion trees and bottom-up matrices.
  • Tabulate animation trace highlights showing element-by-element filling.
  • Execution console logging active sub-states value changes.
04. Architecture

DP States Transitions

Target N input selection
Bottom-up tabulation steps loop
Split-screen visualization panels
05. Tech Stack

Built With

React 19TypeScriptTailwind CSSFramer MotionGitHub
06 & 07. Links

Source & Deploy

GitHub Repository:landing
Live Lab Endpoint:/labs/dp-visualizer
08. Interview Explanation

Defending the Design

Interview Defense Strategy

I implemented the DP Visualizer to compare top-down memoization trees and bottom-up tabulation tables side-by-side. Seeing redundant sub-branches get pruned and tabulation tables fill cell-by-cell helps developers master dynamic programming intuitively.
09. Future Improvements

Roadmap Extensions

  • Add 2D Knapsack grid animation comparing weight capacities.
  • Implement Longest Common Subsequence matrix path tracers.
  • Include space complexity optimizations visual transformations.