Adaptive Variant of the Frank–Wolfe Algorithm for Convex Optimization Problems
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Published:2023-12
Issue:6
Volume:49
Page:493-504
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ISSN:0361-7688
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Container-title:Programming and Computer Software
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language:en
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Short-container-title:Program Comput Soft
Author:
Aivazian G. V.ORCID, Stonyakin F. S.ORCID, Pasechnyk D. A.ORCID, Alkousa M. S.ORCID, Raigorodsky A. M.ORCID, Baran I. V.ORCID
Publisher
Pleiades Publishing Ltd
Reference20 articles.
1. Canon, M.D. and Cullum, C.D., A tight upper bound on the rate of convergence of Frank–Wolfe algorithm, SIAM J. Control, 1968, vol. 6, no. 4, pp. 509–516. 2. Bomze, I.M., Rinaldi, F., and Zeffiro, D., Frank–Wolfe and friends: A journey into projection-free first-order optimization methods, 4OR-Q. J.
Oper. Res., 2021, vol. 19, pp. 313–345. 3. Braun, G., Carderera, A., Combettes, C.W., Hassani, H., Karbasi, A., Mokhtari, A., and Pokutta, S., Conditional gradient methods.
https://arxiv.org/pdf/2211.14103.pdf. 4. Nesterov, Y., Complexity bounds for primal-dual methods minimizing the model of objective function, Math. Program., 2018, vol. 171, nos. 1–2, pp. 311–330. 5. Nesterov, Y., Universal gradient methods for convex optimization problems, Math. Program., 2015, vol. 152, pp. 381–404.
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