Data Structures & Algorithms

Mapping terms to document postings for efficient full-text search.

LRU, LFU, FIFO, random and modern approximate policies, with workload-dependent hit-rate trade-offs.

Solving overlapping subproblems with memoization/tabulation by defining state, transition, initialization, and answer.

Making locally optimal choices only when the problem structure proves they compose into a global optimum.

Split a problem into smaller independent subproblems, solve them, then combine their results.

Recursive problem decomposition, base cases, call-stack cost, tail recursion caveats, and iterative alternatives.

Search a decision tree by choose–recurse–undo, with pruning and state management.

Recursive or explicit-stack DFS for graphs/trees, visitation state, cycle handling, and complexity.

Logarithmic search over a monotonic/sorted domain, including boundary variants.

Adjacent-swap sorting with quadratic time and mainly educational value.