Groups
Category
Level
Quantum computing uses qubits that can be in superpositions, enabling interference-based computation beyond classical bits.
Streaming algorithms process massive data one pass at a time using sublinear—often polylogarithmic—memory.
Distributed algorithm theory studies how many independent computers cooperate correctly and efficiently despite delays and failures.
Parallel algorithm theory studies how to solve problems faster by coordinating many processors that share work and memory.
Algorithmic Information Theory studies information content via the shortest programs that generate data, rather than via average-case probabilities.
Online algorithms make decisions step by step without seeing the future and are judged against an all-knowing offline optimum.
Approximation algorithms deliver provably near-optimal solutions for NP-hard optimization problems within guaranteed factors.
Randomized algorithms use random bits to make choices that simplify design, avoid worst cases, and often speed up computation.
Amortized analysis measures the average cost per operation over a worst-case sequence, not over random inputs.
Computability theory studies the boundary between what can and cannot be computed by any algorithm.
Optimal Transport (OT) formalizes the cheapest way to move one probability distribution into another given a cost to move mass.
The Halting Problem asks whether a given program P will eventually stop when run on input x; there is no algorithm that correctly answers this for all P and x.