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- W2897413842 abstract "We describe convergence acceleration schemes for multistep optimization algorithms. The extrapolated solution is written as a nonlinear average of the iterates produced by the original optimization method. Our analysis does not need the underlying fixed-point operator to be symmetric, hence handles e.g. algorithms with momentum terms such as Nesterov's accelerated method, or primal-dual methods. The weights are computed via a simple linear system and we analyze performance in both online and offline modes. We use Crouzeix's conjecture to show that acceleration performance is controlled by the solution of a Chebyshev problem on the numerical range of a non-symmetric operator modeling the behavior of iterates near the optimum. Numerical experiments are detailed on logistic regression problems." @default.
- W2897413842 created "2018-10-26" @default.
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- W2897413842 date "2022-02-09" @default.
- W2897413842 modified "2023-09-27" @default.
- W2897413842 title "Nonlinear Acceleration of Momentum and Primal-Dual Algorithms" @default.
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- W2897413842 doi "https://doi.org/10.1007/s10107-022-01775-x" @default.
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