CRM initialized in V converges linearly at the sharp rate ρ_V = (sin²θ_p - sin²θ_F)/(sin²θ_p + sin²θ_F) which is optimal for parameter-free single-step methods and smaller than c_F².
Computational Optimization and Applications 79(2), 507–530 (2021)
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Investigates Fejér* monotonicity in Hilbert spaces for optimization algorithms, its weak and strong convergence, and comparisons to quasi-Fejér-type notions via examples.
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On the sharp linear convergence rate of the circumcentered--reflection method on subspaces
CRM initialized in V converges linearly at the sharp rate ρ_V = (sin²θ_p - sin²θ_F)/(sin²θ_p + sin²θ_F) which is optimal for parameter-free single-step methods and smaller than c_F².
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Fej\'er* monotonicity in optimization algorithms
Investigates Fejér* monotonicity in Hilbert spaces for optimization algorithms, its weak and strong convergence, and comparisons to quasi-Fejér-type notions via examples.