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Longest Increasing Subsequence: O(n^2) and O(n log n) Patience

This generated surface maps a learner-facing curriculum unit to its canonical source routes.

Curriculum surface​

  • Open learner-facing unit
  • Curriculum path: content/curriculum/foundations/semester-02-algorithms/module-04-dynamic-programming/concepts/cluster-02-linear-and-sequence-dp/06-longest-increasing-subsequence-n2-and-nlogn-patience-primary.md
  • App: foundations
  • Semester: semester-02-algorithms
  • Module: module-04-dynamic-programming
  • Unit kind: concept
  • Curation level: generated_default

Learning objectives​

  • Explain Longest Increasing Subsequence: O(n^2) and O(n log n) Patience in the language of the current curriculum, not just the source book.
  • Apply Longest Increasing Subsequence: O(n^2) and O(n log n) Patience to one concrete learner task or example inside this semester.
  • Use competitive-programming, introduction-to-algorithms-clrs, the-algorithm-design-manual as a selective source of truth when the learner-facing explanation is not enough.

Prerequisites​

  • The earlier concept pages and practice tasks in the current module.

Source books​

  • competitive-programming
  • introduction-to-algorithms-clrs
  • the-algorithm-design-manual

Source routes​

Competitive Programming​

Introduction To Algorithms Clrs​

The Algorithm Design Manual​

Supporting curriculum routes​

No supporting curriculum routes linked yet.

External enrichment​

No curated enrichment resources yet.

AI companion modes​

  • Explain simply
  • Socratic tutor
  • Quiz me
  • Challenge my understanding
  • Diagnose my confusion
  • Generate extra practice
  • Revision mode
  • Connect forward / backward

Source-of-truth note​

This teaching unit is learner-facing guidance assembled from multiple canonical book routes. Use the listed source books as the primary conceptual spine for Longest Increasing Subsequence: O(n^2) and O(n log n) Patience, and treat outside material as supporting enrichment only.