A globally convergent inertial first-order optimization method for multidimensional scaling
From MaRDI portal
Publication:6608762
DOI10.1007/s10957-024-02486-3MaRDI QIDQ6608762
Publication date: 20 September 2024
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
global convergencemultidimensional scalingfirst-order methodsmajorization minimizationnonconvex and nonsmooth optimization
Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10) Numerical methods based on nonlinear programming (49M37)
Cites Work
- Unnamed Item
- Unnamed Item
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Convergence of the majorization method for multidimensional scaling
- On gradients of functions definable in o-minimal structures
- Modern multidimensional scaling. Theory and applications.
- Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
- Multidimensional scaling. I: Theory and method
- MM Optimization Algorithms
- iPiano: Inertial Proximal Algorithm for Nonconvex Optimization
- Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems
- Variational Analysis
- First Order Methods Beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems
- Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
- The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems
- Some methods of speeding up the convergence of iteration methods
This page was built for publication: A globally convergent inertial first-order optimization method for multidimensional scaling