Accelerated Fixed Point Algorithms for Convex Optimization Using Extreme Learning Machine Models
DOI:
https://doi.org/10.64060/JESTT3i32Keywords:
Accelerated fixed point algorithm, nonexpansive mappings, common fixed point, convex minimizationAbstract
In this paper, a common fixed point of a countable set of nonexpansive mappings in Hilbert space. Under appropriate regulatory circumstances, the weak convergence theorems are formulated. Nonsmoothed convex minimization problems, which are expressed as the union of two suitable convex lower semicontinuous functions, are tackled by applying the proposed method. The subsequent method provides a viable answer to structured convex optimization problems that arise in many engineering and scientific settings; it is constructed within the forward-backwards splitting paradigm. Also, using an extreme learning machine model with l1-regularization, we show that it works well for machine learning classification tasks. Improving forecast accuracy through rapid iterative refinement, the proposed method deftly computes output weights by tackling the basic convex optimization problem. Its effectiveness, durability, and improved classification accuracy compared to existing methods are confirmed by numerical experiments on many benchmark datasets.
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Copyright (c) 2026 Natasha Seemab, Suthep Suantai, Muhammad Jamshed Abbass (Author)
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