Randomized Algorithms
Randomized algorithms are computational procedures that incorporate randomness (e.g., random numbers) into their logic to solve problems, often achieving better performance, simplicity, or reliability than deterministic alternatives. They are used in areas like optimization, cryptography, and data analysis, where probabilistic guarantees (e.g., expected runtime or success probability) are acceptable. Examples include Monte Carlo methods for approximation and Las Vegas algorithms that always produce correct results but with random runtime.
Developers should learn randomized algorithms when dealing with NP-hard problems, large datasets, or scenarios where approximate solutions are sufficient, as they can provide faster or more practical solutions than exact deterministic methods. They are essential in fields like machine learning (e.g., stochastic gradient descent), randomized data structures (e.g., Bloom filters), and security (e.g., cryptographic protocols), offering trade-offs between accuracy and efficiency.