A recent proximal gradient algorithm for convex minimization problem using double inertial extrapolations
Copyright policy )A recent proximal gradient algorithm for convex minimization problem using double inertial extrapolations
<abstract><p>In this study, we suggest a new class of forward-backward (FB) algorithms designed to solve convex minimization problems. Our method incorporates a linesearch technique, eliminating the need to choose Lipschitz assumptions explicitly. Additionally, we apply double inertial extrapolations to enhance the algorithm's convergence rate. We establish a weak convergence theorem under some mild conditions. Furthermore, we perform numerical tests, and apply the algorithm to image restoration and data classification as a practical application. The experimental results show our approach's superior performance and effectiveness, surpassing some existing methods in the literature.</p></abstract>
- Chiang Mai University Thailand
- University of Phayao Thailand
Artificial intelligence, Inverse Problems in Mathematical Physics and Imaging, Convex Optimization, Economics, Inverse Problems, minimization problem, Computational Mechanics, Biomedical Engineering, Geometry, FOS: Medical engineering, Mathematical analysis, Quantum mechanics, Sparse Approximation, inertial extrapolation, Engineering, Compressed Sensing, Convex function, QA1-939, FOS: Mathematics, Image (mathematics), Orthogonal Matching Pursuit, Mathematical Physics, Economic growth, Computer network, forward-backward algorithm, Minification, Physics, Mathematical optimization, Advances in Photoacoustic Imaging and Tomography, Lipschitz continuity, Rate of convergence, Theory and Applications of Compressed Sensing, Computer science, Convex optimization, Regular polygon, Algorithm, Channel (broadcasting), Physical Sciences, Convergence (economics), weak convergence, Inertial frame of reference, Mathematics
Artificial intelligence, Inverse Problems in Mathematical Physics and Imaging, Convex Optimization, Economics, Inverse Problems, minimization problem, Computational Mechanics, Biomedical Engineering, Geometry, FOS: Medical engineering, Mathematical analysis, Quantum mechanics, Sparse Approximation, inertial extrapolation, Engineering, Compressed Sensing, Convex function, QA1-939, FOS: Mathematics, Image (mathematics), Orthogonal Matching Pursuit, Mathematical Physics, Economic growth, Computer network, forward-backward algorithm, Minification, Physics, Mathematical optimization, Advances in Photoacoustic Imaging and Tomography, Lipschitz continuity, Rate of convergence, Theory and Applications of Compressed Sensing, Computer science, Convex optimization, Regular polygon, Algorithm, Channel (broadcasting), Physical Sciences, Convergence (economics), weak convergence, Inertial frame of reference, Mathematics
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