Computational complexity

Integer linear programming can help find the answer to a variety of real-world problems. Now researchers have found a much faster way to do it.

Locally correctable codes need barely any information to fix errors, but they’re extremely long. Now we know that the simplest versions can’t get any shorter.

Artificial intelligence learned how to generate text and art better than ever before, while computer scientists developed algorithms that solved long-standing problems.

Researchers prove that navigating certain systems of vectors is among the most complex computational problems.

Mathematical proofs based on a technique called diagonalization can be relentlessly contrarian, but they help reveal the limits of algorithms.

How hard is it to prove that problems are hard to solve? Meta-complexity theorists have been asking questions like this for decades. A string of recent results has started to deliver answers.

Quantum algorithms can find their way out of mazes exponentially faster than classical ones, at the cost of forgetting the path they took. A new result suggests that the trade-off may be inevitable.

A new algorithm brings together the advantages of randomness and deterministic processes to reliably construct large prime numbers.

A new paper finds a faster method for determining when two mathematical groups are the same.