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Concepts17

Groups

📐Linear Algebra15📈Calculus & Differentiation10🎯Optimization14🎲Probability Theory12📊Statistics for ML9📡Information Theory10🔺Convex Optimization7🔢Numerical Methods6🕸Graph Theory for Deep Learning6🔵Topology for ML5🌐Differential Geometry6∞Measure Theory & Functional Analysis6🎰Random Matrix Theory5🌊Fourier Analysis & Signal Processing9🎰Sampling & Monte Carlo Methods10🧠Deep Learning Theory12🛡️Regularization Theory11👁️Attention & Transformer Theory10🎨Generative Model Theory11🔮Representation Learning10🎮Reinforcement Learning Mathematics9🔄Variational Methods8📉Loss Functions & Objectives10⏱️Sequence & Temporal Models8💎Geometric Deep Learning8

Category

🔷All∑Math⚙️Algo🗂️DS📚Theory

Level

AllBeginner
⚙️AlgorithmIntermediate

Think Backwards (Reverse Thinking)

Think Backwards is a problem‑solving pattern where you reverse time or direction so hard deletions become easy insertions and the final state becomes the starting point.

#think backwards#reverse thinking#offline queries+12
⚙️AlgorithmIntermediate

Bidirectional BFS (Meet in the Middle Search)

Bidirectional BFS searches forward from the start and backward from the goal to meet in the middle, drastically reducing explored states.

#bidirectional bfs
12
Intermediate
Advanced
Filtering by:
#graph algorithms
#meet in the middle
#two-ended search
+11
⚙️AlgorithmIntermediate

Tree DP - Rerooting Technique

Rerooting (a.k.a. 换根 DP) computes a per-node answer as if each node were the root, in total O(n) time on trees.

#rerooting#tree dp#prefix suffix+11
⚙️AlgorithmIntermediate

DP on Trees

DP on trees is a technique that computes answers for each node by combining results from its children using a post-order DFS.

#tree dp#post-order dfs#rerooting+12
⚙️AlgorithmIntermediate

2-SAT

2-SAT solves Boolean formulas where every clause has exactly two literals, and it is solvable in linear time relative to the size of the implication graph.

#2-sat#implication graph#strongly connected components+12
⚙️AlgorithmIntermediate

Bipartite Matching - Hopcroft-Karp

Hopcroft–Karp computes maximum matching in a bipartite graph in O(E \sqrt{V}) time, which is asymptotically faster than repeated DFS (Kuhn's algorithm).

#hopcroft karp#bipartite matching#augmenting path+11
⚙️AlgorithmIntermediate

Bipartite Matching - Kuhn's Algorithm

Kuhn’s algorithm finds a maximum matching in a bipartite graph by repeatedly searching for augmenting paths using DFS.

#bipartite matching#kuhn algorithm#augmenting path+12
⚙️AlgorithmIntermediate

Maximum Flow - Ford-Fulkerson

Ford–Fulkerson finds the maximum possible flow from a source to a sink by repeatedly pushing flow along an augmenting path in the residual graph.

#maximum flow#ford-fulkerson#edmonds-karp+10
⚙️AlgorithmIntermediate

Tarjan's SCC Algorithm

Tarjan’s algorithm finds all Strongly Connected Components (SCCs) of a directed graph in a single depth-first search using a stack.

#tarjan scc#strongly connected components#low link+12
⚙️AlgorithmIntermediate

SPFA (Shortest Path Faster Algorithm)

SPFA is a queue-based optimization of Bellman–Ford that only relaxes edges from vertices whose distance just improved.

#spfa#bellman-ford#shortest path+12
⚙️AlgorithmIntermediate

MST Properties and Applications

An MST minimizes total edge weight over all spanning trees and has powerful properties such as the cut and cycle properties that guide correct, greedy construction.

#minimum spanning tree#kruskal#prim+12
⚙️AlgorithmIntermediate

Minimum Spanning Tree - Prim

Prim's algorithm builds a Minimum Spanning Tree (MST) by growing a tree from an arbitrary start vertex, always adding the lightest edge that connects the tree to a new vertex.

#prim#minimum spanning tree#mst+12