mlsanityData to 7 Oct 2026

From Halpern's Fixed-Point Iterations to Nesterov's Accelerated Interpretations for Root-Finding Problems

arXiv 2203.04869 ↗First posted 9 Mar 2022math.OCcited by 18

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Abstract (arXiv)

We derive an equivalent form of Halpern's fixed-point iteration scheme for solving a co-coercive equation (also called a root-finding problem), which can be viewed as a Nesterov's accelerated interpretation. We show that one method is equivalent to another via a simple transformation, leading to a straightforward convergence proof for Nesterov's accelerated scheme. Alternatively, we directly establish convergence rates of Nesterov's accelerated variant, and as a consequence, we obtain a new convergence rate of Halpern's fixed-point iteration. Next, we apply our results to different methods to solve monotone inclusions, where our convergence guarantees are applied. Since the gradient/forward scheme requires the co-coerciveness of the underlying operator, we derive new Nesterov's accelerated variants for both recent extra-anchored gradient and past-extra anchored gradient methods in the literature. These variants alleviate the co-coerciveness condition by only assuming the monotonicity and Lipschitz continuity of the underlying operator. Interestingly, our new Nesterov's accelerated interpretation of the past-extra anchored gradient method involves two past-iterate correction terms. This formulation is expected to guide us developing new Nesterov's accelerated methods for minimax problems and their continuous views without co-coericiveness. We test our theoretical results on two numerical examples, where the actual convergence rates match well the theoretical ones up to a constant factor.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Mar 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 3; 2025: 2; 2026: 5.

18 papers.

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  1. 2022Accelerated Single-Call Methods for Constrained Min-Max Optimizationcited by 31
  2. 2022Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusioncited by 19
  3. 2025Towards Weaker Variance Assumptions for Stochastic Optimizationcited by 13
  4. 2023Doubly Optimal No-Regret Learning in Monotone Gamescited by 12
  5. 2023Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problemscited by 9
  6. 2023Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developmentscited by 5
  7. 2026Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularizationcited by 3
  8. 2024An Inexact Halpern Iteration with Application to Distributionally Robust Optimizationcited by 2
  9. 2024Variance-Reduced Fast Krasnoselkii-Mann Methods for Finite-Sum Root-Finding Problemscited by 2
  10. 2022Primal-dual extrapolation methods for monotone inclusions under local Lipschitz continuitycited by 1
  11. 2022Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysiscited by 1
  12. 2025A Class of Accelerated Fixed-Point-Based Methods with Delayed Inexact Oracles and Its Applicationscited by 1
6 more
  1. 2023Extragradient-Type Methods with O (1/k) Last-Iterate Convergence Rates for Co-Hypomonotone Inclusionscited by 0
  2. 2024HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image Restorationcited by 0
  3. 2026RAMPAGE: RAndomized Mid-Point for debiAsed Gradient Extrapolationcited by 0
  4. 2026On Same-Sample and Independent-Sample Stochastic Extragradient for Monotone Variational Inequalitiescited by 0
  5. 2026Provable Parameter-Free Fixed-Point Algorithms with Linear Convergence Ratescited by 0
  6. 2026Polyak-Type Extragradient Methods for Monotone Root-Finding Problemscited by 0