Incremental Quasi-Subgradient Method for Minimizing Sum of Geodesic Quasi-Convex Functions on Riemannian Manifolds with Applications
Author:
Affiliation:
1. Department of Mathematics, Aligarh Muslim University, Aligarh, India
2. Department of Applied Mathematics, Z.H. College of Engineering & Technology, Aligarh Muslim University, Aligarh, India
Funder
DST-SERB
Publisher
Informa UK Limited
Subject
Control and Optimization,Computer Science Applications,Signal Processing,Analysis
Link
https://www.tandfonline.com/doi/pdf/10.1080/01630563.2021.2001823
Reference38 articles.
1. Fixed Point Optimization Algorithms for Distributed Optimization in Networked Systems
2. Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning
3. A New Incremental Optimization Algorithm for ML-Based Source Localization in Sensor Networks
4. Convergence of Approximate and Incremental Subgradient Methods for Convex Optimization
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1. Proximal Point Method for Quasiconvex Functions in Riemannian Manifolds;Journal of Optimization Theory and Applications;2024-06-27
2. Karush-Kuhn-Tucker optimality conditions for non-smooth geodesic quasi-convex optimization on Riemannian manifolds;Optimization;2023-07-13
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