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Convergence of Constrained Anderson Acceleration
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Convergence of Constrained Anderson Acceleration
abstract
We prove non asymptotic linear convergence rates for the constrained Anderson acceleration extrapolation scheme. These guarantees come from new upper bounds on the constrained Chebyshev problem, which consists in minimizing the maximum absolute value of a polynomial on a bounded real interval with $l_1$ constraints on its coefficients vector. Constrained Anderson Acceleration has a numerical cost comparable to that of the original scheme.
Forward citations
Cited by 1 Pith paper
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Restart and Adaptive Acceleration in Stochastic Gradient Methods
Restart schemes for SGD on KL-satisfying non-smooth weakly convex problems deliver accelerated convergence robust to exponent misspecification, with optimal schedules resembling Polyak steps.
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