Algorithms

Core: start here

• Big O NotationDescribing how runtime or memory grows with input size.

• Amortized AnalysisThe average cost per operation over a sequence of operations.
• BacktrackingTrying choices and undoing them when they lead nowhere.
• Big Omega and Big ThetaLower bounds and tight bounds, alongside Big O's upper bound.
• Breadth-First SearchExploring a graph level by level; finds shortest unweighted paths.
• Depth-First SearchExploring a graph as deep as possible before backtracking.
• Divide and ConquerSplitting a problem into smaller ones and combining the results.
• Dynamic ProgrammingSolving overlapping subproblems once and reusing the answers.
• Greedy AlgorithmTaking the locally best choice at each step.
• Merge SortSplit in half, sort each half, merge: a stable O(n log n) sort.
• QuicksortPartitioning around a pivot; fast in practice, O(n²) in the worst case.
• Sliding WindowMaintaining a moving range over a sequence.
• Stable SortA sort that keeps equal elements in their original order.
• Topological SortOrdering tasks so every dependency comes first.
• Two PointersWalking two indexes through data to avoid nested loops.

• Bellman-Ford AlgorithmShortest paths that also handles negative edge weights.
• Classic Hard ProblemsKnapsack, traveling salesman and others, and how to recognize them in disguise.
• Consistent HashingHashing that moves only a few keys when servers are added or removed.
• Diff AlgorithmsComputing the minimal set of changes between two sequences.
• Dijkstra's AlgorithmFinding shortest paths in a graph with non-negative weights.
• Huffman CodingCompressing data by giving frequent symbols shorter codes.
• Minimum Spanning TreeConnecting all nodes with the least total edge weight (Prim's, Kruskal's).
• P vs NP / NP-CompleteProblems with no known efficient solution, and why some tasks stay hard.
• String SearchingAlgorithms like KMP and Rabin-Karp for finding substrings.

Nothing here yet.

• Maximum FlowFinding the most that can flow through a network (Ford-Fulkerson).

• AlgorithmA step-by-step procedure for solving a problem.
• Best, Worst and Average CaseDifferent inputs can make the same algorithm fast or slow.
• Big O NotationDescribing how runtime or memory grows with input size.
• Binary SearchFinding an item in sorted data by halving the range each step.
• Brute ForceTrying every possibility; the baseline before optimizing.
• Coding Interview ProblemsAlgorithm puzzles used in hiring, and how they relate to real work.
• Common Complexity ClassesO(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ) and what they feel like in practice.
• Linear SearchChecking every element in turn.
• Sorting AlgorithmsBubble, insertion, merge, quick and heap sort, and their trade-offs.
• Space ComplexityHow memory use grows with input size.
• Time ComplexityHow the number of steps grows with input size.

• Amortized AnalysisThe average cost per operation over a sequence of operations.
• BacktrackingTrying choices and undoing them when they lead nowhere.
• Big Omega and Big ThetaLower bounds and tight bounds, alongside Big O's upper bound.
• Breadth-First SearchExploring a graph level by level; finds shortest unweighted paths.
• Depth-First SearchExploring a graph as deep as possible before backtracking.
• Divide and ConquerSplitting a problem into smaller ones and combining the results.
• Dynamic ProgrammingSolving overlapping subproblems once and reusing the answers.
• Greedy AlgorithmTaking the locally best choice at each step.
• Merge SortSplit in half, sort each half, merge: a stable O(n log n) sort.
• QuicksortPartitioning around a pivot; fast in practice, O(n²) in the worst case.
• Sliding WindowMaintaining a moving range over a sequence.
• Stable SortA sort that keeps equal elements in their original order.
• Topological SortOrdering tasks so every dependency comes first.
• Two PointersWalking two indexes through data to avoid nested loops.

• Bellman-Ford AlgorithmShortest paths that also handles negative edge weights.
• Classic Hard ProblemsKnapsack, traveling salesman and others, and how to recognize them in disguise.
• Consistent HashingHashing that moves only a few keys when servers are added or removed.
• Diff AlgorithmsComputing the minimal set of changes between two sequences.
• Dijkstra's AlgorithmFinding shortest paths in a graph with non-negative weights.
• Huffman CodingCompressing data by giving frequent symbols shorter codes.
• Minimum Spanning TreeConnecting all nodes with the least total edge weight (Prim's, Kruskal's).
• P vs NP / NP-CompleteProblems with no known efficient solution, and why some tasks stay hard.
• String SearchingAlgorithms like KMP and Rabin-Karp for finding substrings.

Nothing here yet.

• Maximum FlowFinding the most that can flow through a network (Ford-Fulkerson).

• AlgorithmA step-by-step procedure for solving a problem.
• Best, Worst and Average CaseDifferent inputs can make the same algorithm fast or slow.
• Big O NotationDescribing how runtime or memory grows with input size.
• Binary SearchFinding an item in sorted data by halving the range each step.
• Brute ForceTrying every possibility; the baseline before optimizing.
• Coding Interview ProblemsAlgorithm puzzles used in hiring, and how they relate to real work.
• Common Complexity ClassesO(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ) and what they feel like in practice.
• Linear SearchChecking every element in turn.
• Sorting AlgorithmsBubble, insertion, merge, quick and heap sort, and their trade-offs.
• Space ComplexityHow memory use grows with input size.
• Time ComplexityHow the number of steps grows with input size.

• Amortized AnalysisThe average cost per operation over a sequence of operations.
• BacktrackingTrying choices and undoing them when they lead nowhere.
• Big Omega and Big ThetaLower bounds and tight bounds, alongside Big O's upper bound.
• Breadth-First SearchExploring a graph level by level; finds shortest unweighted paths.
• Depth-First SearchExploring a graph as deep as possible before backtracking.
• Divide and ConquerSplitting a problem into smaller ones and combining the results.
• Dynamic ProgrammingSolving overlapping subproblems once and reusing the answers.
• Greedy AlgorithmTaking the locally best choice at each step.
• Merge SortSplit in half, sort each half, merge: a stable O(n log n) sort.
• QuicksortPartitioning around a pivot; fast in practice, O(n²) in the worst case.
• Sliding WindowMaintaining a moving range over a sequence.
• Stable SortA sort that keeps equal elements in their original order.
• Topological SortOrdering tasks so every dependency comes first.
• Two PointersWalking two indexes through data to avoid nested loops.

• Classic Hard ProblemsKnapsack, traveling salesman and others, and how to recognize them in disguise.
• Diff AlgorithmsComputing the minimal set of changes between two sequences.
• Dijkstra's AlgorithmFinding shortest paths in a graph with non-negative weights.
• Huffman CodingCompressing data by giving frequent symbols shorter codes.
• Minimum Spanning TreeConnecting all nodes with the least total edge weight (Prim's, Kruskal's).
• P vs NP / NP-CompleteProblems with no known efficient solution, and why some tasks stay hard.
• String SearchingAlgorithms like KMP and Rabin-Karp for finding substrings.

Nothing here yet.

Nothing here yet.