US DOE Announces New Funding for Studying Randomized Algorithms

The US Department of Energy (DOE) has announced US$10Million in funding for “basic research in the design, development, and scalability of randomized algorithms for scientific computing.”

According to the DOE, “Randomized algorithms employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems and stochastic gradient descent for training scientific machine learning models. Rather than using heuristic or ad-hoc methods, the desired objective is the development of efficient randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality or near-optimality.”

The hope is that better and more efficient randomized algorithms could lead to better and faster solutions to complex problems in scientific programming. Proposals for projects with funding for up to three years are open to universities, non-profits, for-profit entities, DOE-NNSA labs, and federal agencies. For more information, download the DOE announcement.