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R. Gower, D. Molitor, J. Moorman, and D. Needell
Original
2019
Later among the works it cites.
J. Haddock and A. Ma
Original
2019
Later among the works it cites.
I. Necoara
2019
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P.-G. Martinsson and J. A. Tropp
2020
Later among the works it cites.
P. Richtárik and M. Takác
2020
Later among the works it cites.
S. Wright and C.-p. Lee
2020
Later among the works it cites.
R. M. Gower, D. Molitor, J. Moorman, and D. Needell

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2022

Patel, Vivak, Jahangoshahi, Mohammad, Maldonado, Daniel Adrian

Randomized linear solvers randomly compress and solve a linear system with compelling theoretical convergence rates and computational complexities.

However, such solvers suffer a substantial disconnect between their theoretical rates and actual efficiency in practice.
Fortunately, these solvers are quite flexible and can be adapted to specific problems and computing environments to ensure high efficiency in practice, even at the cost of lower effectiveness (i.e., having a slower theoretical rate of convergence).
While highly efficient adapted solvers can be readily designed by application experts, will such solvers still converge and at what rate? To answer this, we distill three general criteria for randomized adaptive solvers, which, as we show, will guarantee a worst-case exponential rate of convergence of the solver applied to consistent and inconsistent linear systems irrespective of whether such systems are over-determined, under-determined or rank-deficient.

R. Gower, D. Molitor, J. Moorman, and D. Needell
Original
2019
Later among the works it cites.
J. Haddock and A. Ma
Original
2019
Later among the works it cites.
I. Necoara
2019
Later among the works it cites.
P.-G. Martinsson and J. A. Tropp
2020
Later among the works it cites.
P. Richtárik and M. Takác
2020
Later among the works it cites.
S. Wright and C.-p. Lee
2020
Later among the works it cites.
R. M. Gower, D. Molitor, J. Moorman, and D. Needell