This article by Zelda B. Zabinsky provides an overview of random search algorithms, which are particularly useful for tackling complex global optimization problems with either continuous or discrete variables. These algorithms, including simulated annealing, genetic algorithms, and particle swarm optimization, leverage randomness or probability in their iterative processes, often falling under the category of metaheuristics. Such methods are valuable for problems characterized by nonconvex, nondifferentiable, or discontinuous objective functions, as they offer a trade-off between optimality and computational speed. Random search algorithms can be categorized by their approach to exploration versus exploitation, and their application spans various fields, including engineering, scheduling, and biological systems. They address challenges where traditional deterministic methods struggle, particularly in the absence of clear structures distinguishing local from global optima.
A young computer scientist at Rutgers University, along with his former professor and a colleague from Carnegie Mellon University, has disproved a 40-year-old conjecture in data science related to hash tables, showing that a new type can achieve faster search times than previously thought possible.
Andrew Krapivin, an undergraduate student at Rutgers University, along with his colleagues Martín Farach-Colton and William Kuszmaul, has challenged a 40-year-old conjecture by demonstrating that a new type of hash table can perform searches and insertions faster than previously thought possible. Their invention, inspired by "tiny pointers," contradicts a long-standing assumption that the time complexity for these operations would be proportional to the table's fullness (measured as x). Instead, they found that the time complexity for their new hash table is proportional to (log x)^2, which is significantly faster. Additionally, their study also refutes another conjecture regarding the average time taken for queries in non-greedy hash tables, showing that a constant average query time is achievable regardless of the table's fullness.
A method called location arithmetic, first described by John Napier in 1617, uses a checkerboard to perform various mathematical calculations, including multiplication, division, and taking the square root, by breaking numbers into their binary equivalents and moving markers around the board.
