Learning Resources
Use Reading Guide for the core book assignments and Guided Labs for the runnable package. Reinforcement should fix a specific failed explanation or task, not create an unbounded reading backlog.
Book backbone
- Introduction to Algorithms: correctness, sorting bounds, order statistics, hashing.
- The Algorithm Design Manual: problem selection, practical sorting contracts, heaps.
- Algorithms, Sedgewick and Wayne: concrete implementations and symbol-table/PQ interfaces.
Book links are indexes. The reading guide names topics and local files without claiming that a generated route identifies a precise section.
Read-If-Curious: primary sources
- Princeton: Quicksort: inspect partition boundaries and equal-key handling, then explain the three-way invariant.
- Princeton: Hash Tables: compare collision strategies and their assumptions with the teaching map's deliberately deterministic hash.
- Princeton: Priority Queues: compare heap construction and operation contracts to Lab D.
- Python's Sorting HOWTO: verify the chosen Python sort's stability and key behavior before making an API claim.
These primary pages were consulted during author review on 2026-09-18. Their implementation details may change. New teaching fixtures and lab implementations are original; the sources support named concepts, not the completeness of every claim in the module. No independent subject review is implied.