Sparse Tables
Sparse Tables answer range min/max in O(1) after O(n log n) prep. Ideal for static data.
Lessons
1. Intro
Why Sparse Tables matter
2. Power-of-2 Ranges
The building blocks
3. Building the Table
DP construction
4. The Overlap Trick
Idempotent operations
5. Precomputing Log Values
Avoiding log calls
6. Problem - Range Minimum Query
The classic RMQ problem
7. Sparse Table RMQ Solution
Complete implementation
8. RMQ Solution
Implement your solution
9. Idempotent Operations
When overlap trick works
10. Non-Idempotent Queries
O(log n) fallback
11. Problem - Range GCD Queries
GCD is idempotent
12. Range GCD Solution
Implement your solution
13. LCA with Sparse Table
Euler tour + RMQ
14. Euler Tour Construction
Building the tour
15. Problem - LCA Queries
O(1) LCA with preprocessing
16. LCA Solution
Implement your solution
17. Range Max with Index
Returning position, not value
18. 2D Sparse Table
Rectangle min/max queries
19. Sparse Table vs Others
Choosing the right tool
20. Problem - Static Range Min
Many queries, no updates
21. Static RMQ Solution
Implement your solution
22. Second Minimum Query
Storing more information
23. Min with Index
Tracking position
24. Disjoint Sparse Tables
For non-idempotent operations
25. Sparse Table for Matrices
2D rectangle queries
26. Problem - Range Xor Queries
XOR is not idempotent
27. Range XOR Solution
Prefix XOR approach
28. Problem - Forest Queries
2D static queries
29. Forest Queries Solution
2D prefix sums
30. Common Sparse Table Mistakes
Debugging tips
31. Space Optimization
When memory matters
32. RMQ to LCA Reduction
The reverse direction
33. Challenge: Multiple Values
Top-k in a range
34. Quiz: Sparse Tables
Test your understanding
35. Section Recap
What you learned
Practice Problems
Perfect introduction to sparse tables with classic RMQ on static array. Standard O(n log n) preprocessing with O(1) queries for learning fundamentals.
Combines LCA with sparse table and tree distance calculations. Teaches how to find equidistant nodes using binary lifting.
Classic LCA problem using sparse table with path queries on trees. Introduces difference arrays on trees combined with LCA.
Demonstrates sparse table for range GCD queries combined with binary search. Shows how GCD is idempotent and perfect for sparse tables.
Creative range GCD with difference arrays. Transforms finding longest subarray with GCD > 1, teaching problem transformation with sparse tables.
Advanced sparse table on trees storing multiple values per node. Combines LCA with binary lifting while maintaining top-k elements.
Uses binary jumping (sparse table) for ancestor queries on trees. Teaches DSU on tree combined with sparse table for counting relatives.
Combines MST with LCA using sparse table to find maximum edge on paths. Shows range maximum queries for tree path optimization.
Sliding window with sparse table for range maximum queries. Demonstrates two-pointer technique with O(1) RMQ in dynamic windows.
Range frequency queries with Mo's algorithm and sparse table optimizations. Great for understanding offline query processing.
Uses monotonic stack to find previous/next smaller elements (related to RMQ). Teaches how range minimum concepts apply to optimization.
Tree DP with rerooting technique using binary lifting concepts. Shows how sparse table principles extend to DP on trees.
Pure binary lifting implementation, the foundation of sparse tables on trees. Perfect for O(n log n) preprocessing with O(log n) queries.
Classic range maximum solvable with sparse table for O(1) per query. Alternative to deque approach for fixed-size sliding windows.
Offline queries with range operations requiring sorted processing. Demonstrates when sparse table preprocessing concepts apply.
Perfect example of prefix operations for associative functions. Shows when sparse table is overkill versus prefix arrays.
RMQ application finding next smaller elements. Fundamental problem showing how range minimum concepts solve optimization problems.
Range minimum with constraint optimization. Teaches practical RMQ where minimum value in range determines score.
Offline queries with sorted processing (similar to sparse table philosophy). Shows preprocessing for efficient bitwise query answering.
Advanced range query with negative numbers requiring monotonic deque. Demonstrates when simple sparse table fails for non-idempotent operations.