• Media type: E-Article; Text
  • Title: Convergence Rates of First- and Higher-Order Dynamics for Solving Linear Ill-Posed Problems
  • Contributor: Boţ, Radu [Author]; Dong, Guozhi [Author]; Elbau, Peter [Author]; Scherzer, Otmar [Author]
  • imprint: New York, NY : Springer, 2021
  • Published in: Foundations of computational mathematics : FoCM 22 (2022)
  • Issue: published Version
  • Language: English
  • DOI: https://doi.org/10.34657/8109; https://doi.org/10.1007/s10208-021-09536-6
  • Keywords: Dynamical regularisation ; Optimal convergence rates ; Regularisation theory ; Showalter’s method ; Spectral analysis ; Heavy ball method ; Linear ill-posed problems ; Vanishing viscosity flow
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  • Description: Recently, there has been a great interest in analysing dynamical flows, where the stationary limit is the minimiser of a convex energy. Particular flows of great interest have been continuous limits of Nesterov’s algorithm and the fast iterative shrinkage-thresholding algorithm, respectively. In this paper, we approach the solutions of linear ill-posed problems by dynamical flows. Because the squared norm of the residual of a linear operator equation is a convex functional, the theoretical results from convex analysis for energy minimising flows are applicable. However, in the restricted situation of this paper they can often be significantly improved. Moreover, since we show that the proposed flows for minimising the norm of the residual of a linear operator equation are optimal regularisation methods and that they provide optimal convergence rates for the regularised solutions, the given rates can be considered the benchmarks for further studies in convex analysis.
  • Access State: Open Access
  • Rights information: Attribution (CC BY)