A new paper demonstrates that the simplex method, a widely used optimization algorithm, is as efficient as it can be, and explains why it performs well in practice despite theoretical limitations.
An article about Stanislaw Ulam and his development of the Monte Carlo method, its applications in nuclear physics, and its broader impact on various scientific fields.
Ulam developed this method while working on the Manhattan Project during World War II. The technique involves running a large number of random simulations to estimate the likelihood of different outcomes. Initially used to model neutron chain reactions, the Monte Carlo method has since found applications in various fields, including physics, finance, and social sciences. Ulam's work highlighted the importance of embracing uncertainty and leveraging randomness to gain insights into complex systems.
Ulam developed this method while working on the Manhattan Project during World War II. The technique involves running a large number of random simulations to estimate the likelihood of different outcomes. Initially used to model neutron chain reactions, the Monte Carlo method has since found applications in various fields, including physics, finance, and social sciences. Ulam's work highlighted the importance of embracing uncertainty and leveraging randomness to gain insights into complex systems.
In this essay, Lance Fortnow, a computer scientist, argues that by embracing the computations that surround us, we can begin to understand and tame our seemingly random world. He discusses how even seemingly random events, like a coin flip or the mailing of a letter, can be seen as computational processes. The essay also touches on the progress made in artificial intelligence and machine learning, and how they are helping us manage randomness and complexity in our world.
