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Linear Systems and Eigenvalue Problems: Open Questions from a Simons Workshop
Noah Amsel, Yves Baumann, Paul Beckman +33
This document presents a series of open questions arising in matrix computations, i.e., the numerical solution of linear algebra problems. It is a result of working groups at the w…
Beyond Expectation: Concentration Inequalities for Randomized Iterative Methods
Toby Anderson, Max Collins, Jamie Haddock +2
Stochastic iterative methods are useful in a variety of large-scale numerical linear algebraic, machine learning, and statistical problems, in part due to their low-memory footprin…
Subspace-constrained randomized coordinate descent for linear systems with good low-rank matrix approximations
Jackie Lok, Elizaveta Rebrova
The randomized coordinate descent (RCD) method is a classical algorithm with simple, lightweight iterations that is widely used for various optimization problems, including the sol…
A subspace constrained randomized Kaczmarz method for structure or external knowledge exploitation
Jackie Lok, Elizaveta Rebrova
We study a version of the randomized Kaczmarz algorithm for solving systems of linear equations where the iterates are confined to the solution space of a selected subsystem. We sh…