Recorded 23 May 2022. Jonathan Weare of New York University, Mathematics, presents "A fast Jacobi iteration with repeated random sparsification" at IPAM's Monte Carlo and Machine Learning Approaches in Quantum Mechanics Workshop. Abstract: The traditional methods of numerical linear algebra are prohibitively expensive for high-dimensional problems for which even a single matrix multiplication by a dense vector may be too costly. In this talk I will discuss a general framework for reducing the cost of classical iterative schemes like Jacobi iteration by randomly sparsifying the approximate solution at each iteration. I will provide a thorough characterization of the error properties of Jacobi iteration with repeated random sparsification and show results of numerical tests applying the scheme to coupled cluster calculations. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-monte-carlo-and-machine-learning-approaches-in-quantum-mechanics/?tab=schedule