A novel accelerated fixed point algorithm for convex bilevel optimization problems with applications to machine learning for data classification

Fixed point theory and optimization play an important role in machine learning.In this paper, a novel common fixed point algorithm based on the viscosity approximation method and inertial technique is introduced and studied.The strong convergence of the proposed algorithm is established under some control and suitable conditions.As an application, the main result obtained in this work can be applied to solving some convex bilevel optimization problems.Finally, using extreme learning machine model, we employ and compare the performance of our algorithm with the others for data classification problems involving some noncommunication diseases.Our experiments demonstrate that the proposed algorithm has a better convergence behavior than the others in the literature.