Monotone Vector Fields and Proximal Algorithms in G-Metric Spaces: A Comprehensive Framework With Applications to Modern Optimization Challenges

Advances in optimization theory have been made systematically by the desire to solve more and more complicated geometric structures that are realised in contemporary applications. This is a rigorous investigation of monotone vector fields and proximal algorithms in the deep geometrical setting of generalized metric spaces (G-metric spaces). Our study fills a general deficiency in the literature by generalizing classical monotonicity principles and proximal point algorithms to support the complex three-point distance structure of G-metric spaces. In this way, by conducting a strict theoretical study, we prove the existence and uniqueness of solutions in the concept of monotone inclusion, are able to develop effective proximal algorithms with guaranteed convergence rates, and illustrate their successful application in different areas of practice. Theoretical contributions that we have made include: (1) the extension of monotonicity theory in all its forms to G-metric spaces with complete characterizations, (2) the construction of strongly convergent proximal point algorithms that are explicit in rate of convergence, and (3) its application to variational inequalities and multi-objective optimization problems in non-standard geometries, where the old metric structures are no longer applicable. Our findings create new opportunities to deal with optimization problems in complex networks, social systems, and the present-day machine learning paradigms.