Learning Resources
Start with the Reading Guide, Worked Examples and Guided Labs. These sources reinforce identified gaps.
Book backbone
- The Algorithm Design Manual: modeling, traversal, MSTs and flow applications.
- Introduction to Algorithms: invariant and exchange proofs, shortest paths and flow analysis.
- Algorithms, Sedgewick and Wayne: concrete graph interfaces and implementation comparisons.
These are honest book-index links. The reading guide specifies topics and local files; it does not invent precise generated section destinations.
Read-If-Curious: primary sources
- Princeton: Directed Graphs: DFS order, DAGs and SCCs. Compare its Kosaraju pass orientation with the equivalent orientation used in the lab.
- Princeton: Shortest Paths: relaxation and algorithm variants. Apply the course's explicit source/target negative-cycle scope when interpreting broad summaries.
- Princeton: Minimum Spanning Trees: cut-based reasoning and queue variants; track whether a theorem assumes distinct weights.
- Princeton: Maximum Flow: flow implementation and residual-network vocabulary.
- MIT 6.046J lecture 13 notes: flow, residual networks and augmenting-path analysis.
Primary sources were consulted during author review on 2026-09-18. The course's new fixtures and reference code are original. Neither these links nor a passing test suite establish independent review or successful learner pilots.