3.Algorithms

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.

Sequentially scanning elements when no useful index/order exists.

In-place comparison sorting using heap construction followed by repeated root extraction.

Stable O(n log n) divide-and-conquer sorting by merging sorted halves.

Stable in-place insertion into a sorted prefix, efficient for small or nearly sorted inputs.

Partition-based sorting with O(n log n) expected time, pivot selection, and worst-case safeguards.

Repeatedly selecting the minimum/maximum with quadratic comparisons and few swaps.

Comparing sorting algorithms by time, space, stability, adaptiveness, locality, and data constraints.