Coding Concepts

Coding concepts are the tactics behind the problem catalog. A problem category tells you where a problem lives. A concept page tells you what mental move makes the solution work.

The same problem can link to several concepts. That overlap is intentional. 3Sum is sorting as preprocessing plus two pointers. Course Schedule is graph traversal plus topological sort plus cycle detection. Sliding Window Maximum is sliding window plus monotonic queue. The links should explain the transferable ideas, not force every problem into one bucket.

How to use these pages

• Start with the terminology clues when you are stuck on a prompt.
• Check “When not to use it” before committing to a pattern.
• Use the problem links as drills for that tactic.
• Add a problem backlink under ## Related concepts when a page clearly exercises the concept.

Linear and Ordered Data

Concept	What it helps you notice
Array Scans	Linear pass tactics for reading an array once, carrying just enough state, and avoiding nested loops.
Two Pointers	Two-index tactics for shrinking search space while preserving an invariant between positions.
Fast and Slow Pointers	Pointer-speed tactics for cycle detection, middle finding, and linked-list distance constraints.
Sliding Window	Contiguous-range tactics for maintaining a valid subarray or substring while endpoints move forward.
Prefix Sums	Accumulation tactics for answering range-sum and subarray-count questions from differences between checkpoints.
Difference Arrays	Range-update tactics that mark changes at boundaries and recover final values with a prefix scan.
Hash Map Counting	Frequency-table tactics for turning membership, complement, and multiplicity questions into direct lookups.
Sorting as Preprocessing	Order-first tactics that pay O(n log n) so adjacency, monotonic movement, or greedy choice becomes visible.

Search and Ranges

Concept	What it helps you notice
Binary Search	Monotonic search tactics for cutting a sorted or ordered search space in half until one answer remains.
Binary Search on Answer	Feasibility-search tactics for finding the smallest or largest value that satisfies a monotonic condition.
Modified Binary Search	Binary-search variants for rotated arrays, peak finding, and data where the ordering is present but disguised.
Intervals	Range-boundary tactics for overlap, containment, scheduling, and sweep-line problems.
Merge Intervals	Sorted-boundary tactics for combining overlapping ranges and maintaining the current covered span.

Optimization and DP

Concept	What it helps you notice
Greedy Algorithms	Local-choice tactics for solving optimization and reachability problems when a provable invariant protects the future.
Greedy Exchange Arguments	Proof tactics for showing that a greedy choice can be swapped into an optimal solution without making it worse.
Dynamic Programming	State-and-transition tactics for solving overlapping subproblems with cached answers.
Memoization	Top-down caching tactics for preserving recursive clarity while avoiding repeated subproblem work.
Tabulation	Bottom-up DP tactics for filling states in dependency order without recursion.
State Compression	DP memory tactics for keeping only the previous states needed for the next transition.
Knapsack Patterns	Choose-or-skip DP tactics for capacity, subset, and target-sum problems.
Sequence DP	Order-aware DP tactics for strings and arrays where prefixes or positions define reusable states.
Grid DP	Row-column DP tactics for paths, matrix states, and local moves with directional dependencies.

Recursive Search

Concept	What it helps you notice
Backtracking	Search-tree tactics for exploring choices, undoing state, and pruning invalid branches.
Subsets and Combinations	Choice-set tactics for generating selected groups while controlling duplicates and order.
Permutations	Ordering tactics for generating arrangements where the same items in a different order are different answers.
Constraint Search	Pruned search tactics for problems where each choice must satisfy local and global constraints.
Recursion	Self-similar problem-solving tactics for trees, divide-and-conquer, and search branches.
Divide and Conquer	Split-solve-combine tactics for reducing a problem into independent smaller pieces.

Graphs and Trees

Concept	What it helps you notice
Tree Traversal	Recursive and iterative tactics for visiting tree nodes with path, depth, or structural state.
DFS	Depth-first traversal tactics for exploring one branch fully before backtracking to alternatives.
BFS	Breadth-first traversal tactics for level order, shortest unweighted paths, and expanding frontiers.
Graph Traversal	Visited-state tactics for exploring nodes, edges, components, and reachability relationships.
Topological Sort	Dependency-order tactics for DAGs, prerequisites, and detecting cycles in directed graphs.
Union Find	Disjoint-set tactics for tracking connected components as edges arrive.
Shortest Paths	Path-cost tactics for finding minimum distance, time, risk, or transformation count through a graph.
Dijkstra	Non-negative weighted shortest-path tactics using a priority queue frontier.
Bellman-Ford	Repeated-relaxation tactics for shortest paths with negative edges, bounded stops, or layered constraints.
Flood Fill	Grid traversal tactics for expanding through adjacent cells that share a condition.

Specialized Structures

Concept	What it helps you notice
Heap and Priority Queue	Priority-frontier tactics for repeatedly extracting the smallest, largest, or most urgent item.
Top K	Selection tactics for finding the largest, smallest, or most frequent K items without fully ordering everything.
K-way Merge	Multi-stream ordering tactics for combining several sorted sources through one priority queue.
Monotonic Stack	Ordered-stack tactics for nearest greater, nearest smaller, and span-style questions.
Monotonic Queue	Deque tactics for maintaining a window minimum or maximum as the window slides.
Stack Parsing	Last-open-first-closed tactics for nested syntax, expressions, and reversible operations.
Trie Prefix Search	Prefix-tree tactics for sharing common string prefixes and pruning word search branches.

Bits, Math, and Simulation

Concept	What it helps you notice
Bit Manipulation	Binary-representation tactics for masks, toggles, arithmetic shortcuts, and set-like operations.
Bitmask State	Compact-state tactics for representing chosen items, visited sets, and small DP dimensions as integer masks.
Linked List Pointer Rewiring	Node-link tactics for changing list structure without losing the rest of the chain.
Cycle Detection	Repeated-state tactics for finding loops in linked lists, graphs, arrays, and numeric processes.
Simulation	State-machine tactics for faithfully executing rules while keeping state small and explicit.
Math and Number Theory	Arithmetic tactics for problems driven by divisibility, digits, modular behavior, and numeric identities.

References

• NeetCode roadmap, pattern-first problem organization.
• Tech Interview Handbook algorithms guide, interview algorithm and data-structure review.
• AlgoMonster flowchart, decision flow for choosing common interview patterns.
• Grokking the Coding Interview, pattern-based interview preparation.

Related topics

• Coding Problems, the problem catalog these concept pages cross-link with.
• Data Structures, the concrete containers many tactics rely on.
• Named Algorithms, canonical algorithms that deserve deeper standalone treatment.