On some accelerated optimization algorithms based on fixed point and linesearch techniques for convex minimization problems with applications View Full Text


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Article Info

DATE

2022-03-17

AUTHORS

Pornsak Yatakoat, Suthep Suantai, Adisak Hanjing

ABSTRACT

In this paper, we introduce and study a new accelerated algorithm based on forward–backward and SP-algorithm for solving a convex minimization problem of the sum of two convex and lower semicontinuous functions in a Hilbert space. Under some suitable control conditions, a weak convergence theorem of the proposed algorithm based on a fixed point is established. Moreover, we choose the stepsize of our algorithm which is independent on the Lipschitz constant of the gradient of the objective function by using a linesearch technique, and then a weak convergence result of the proposed algorithm is analyzed. As applications, we apply the proposed algorithm for solving the image restoration problems and compare its convergence behavior with other well-known algorithms in the literature. By our experiment, the algorithms have a higher efficiency than the others. More... »

PAGES

25

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13662-022-03698-5

DOI

http://dx.doi.org/10.1186/s13662-022-03698-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1146355840


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