Abstract
We introduce a localized and relativized generalization of the usual concept of Fejer monotonicity together with uniform and quantitative versions thereof and show that the main quantitative results obtained by the first author together with Nicolae and Leustean in 2018 and with Lopez-Acedo and Nicolae in 2019 respectively, extend to this generalization. Our framework, in particular, covers the sequence generated by the Dykstra algorithm while the latter is not Fejer-monotone in the ordinary sense. This gives a theoretical explanation why under a metric regularity assumption one obtains an explicit rate of convergence for Dykstra's algorithm which was proved recently by the second author.
References
- H. H. Bauschke, P. L. Combettes: Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 2nd ed., Springer, New York (2017).https://doi.org/10.1007/978-3-319-48311-5Google Scholar
- R. Behling, Y. Bello-Cruz, A. Iusem, D. Liu, L. R. Santos: A finitely convergent circumcenter method for the convex feasibility problem, SIAM J. Optimization 34 (2024) 2535–2556.https://doi.org/10.1137/23M1595412Google Scholar
- R. Behling, Y. Bello-Cruz, A. Iusem, A. A. Ribeiro, L. R. Santos: Fejér* monotonicity in optimization algorithms, arXiv: 2410.08331 (2024).Google Scholar
- P. L. Combettes: Quasi-Fejérian analysis of some optimization algorithms, in: Studies in Computational Mathematics. Inherently Parallel Algorithms in Feasibility and Optimization and Their Applications Vol. 8, D. Butnariu et al. (eds.), Elsevier, Amsterdam (2001) 115–152.https://doi.org/10.1016/S1570-579X(01)80010-0Google Scholar
- Yu. M. Ermol’ev: On the method of generalized stochastic gradients and quasi-Fejér sequences, Cybernetics 5/2 (1969) 208–220.https://doi.org/10.1007/BF01071091Google Scholar
- F. Iutzeler, J. M. Hendrickx: A generic online acceleration scheme for optimization algorithms via relaxation and inertia, Optim. Methods Software 34 (2019) 383–405.https://doi.org/10.1080/10556788.2017.1396601Google Scholar
- U. Kohlenbach, L. Leuştean, A. Nicolae: Quantitative results on Fejér monotone sequences, Comm. Contemp. Math. 20/2 (2018), art.no. 1750015, 42p.https://doi.org/10.1142/S0219199717500158Google Scholar
- U. Kohlenbach, G. López-Acedo, A. Nicolae: Moduli of regularity and rates of convergence for Fejér monotone sequences, Israel J. Math. 232 (2019) 261–297.https://doi.org/10.1007/s11856-019-1870-xGoogle Scholar
- U. Kohlenbach, N. Pischke: Quantitative results for a Tseng type primal-dual method for composite monotone inclusions, submitted.Google Scholar
- Z. Mu, Y. Peng: A note on the inertial proximal point method, Stat. Optim. Inf. Comput. 3 (2015) 241–248.https://doi.org/10.19139/soic.v3i3.124Google Scholar
- E. Neumann: Computational problems in metric fixed point theory and their Weihrauch degrees, Logical Methods Comp. Sci. 1/41 (2015), art.no. 20, 44p.https://doi.org/10.2168/LMCS-11(4:20)2015Google Scholar
- P. Pinto: Proof mining and the convex feasibility problem : the curious case of Dykstra’s algorithm, Rev. Symb. Log. 18/3 (2025) 775–811.https://doi.org/10.1017/S1755020325100695Google Scholar
- P. Pinto: On the finitary content of Dykstra’s cyclic projections algorithm, Zeitschrift f. Analysis Anwend. 44/1-2 (2025) 165–192.https://doi.org/10.4171/zaa/1768Google Scholar
- P. Pinto, N. Pischke: On Dykstra’s algorithm with Bregman projections, Oberwolfach preprints, OWP-2024-04 (2024).Google Scholar
- N. Pischke: Generalized Fejér monotone sequences and their finitary content, Optimization 74/14 (2025) 3771–3838.https://doi.org/10.1080/02331934.2024.2390114Google Scholar
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Published by Heldermann Verlag, 2026. Rights now held by Banach Press.
