🏠 Home
Benchmark Hub
📊 All Benchmarks 🦖 Dinosaur v1 🦖 Dinosaur v2 ✅ To-Do List Applications 🎨 Creative Free Pages 🎯 FSACB - Ultimate Showcase 🌍 Translation Benchmark
Models
🏆 Top 10 Models 🆓 Free Models 📋 All Models ⚙️ Kilo Code
Resources
💬 Prompts Library 📖 AI Glossary 🔗 Useful Links
advanced

Big-O Optimization for Graph Traversal

#algorithms #optimization #graph-theory #coding

Critique and optimize a poorly written graph traversal algorithm to improve time and space complexity.

Review the following Python code snippet which implements a shortest-path finding algorithm on a weighted graph. The current implementation has a time complexity of O(V^3). Critique the inefficiencies in the current approach, specifically identifying redundant calculations or suboptimal data structures used. Refactor the code to implement Dijkstra's algorithm using a min-heap priority queue, reducing the time complexity to O(E + V log V). Additionally, modify the solution to return not just the distance, but the actual path reconstruction. Provide the optimized code and a step-by-step explanation of how the heap operations maintain the algorithm's invariant.