Separable cubic modeling and a trust-region strategy for unconstrained minimization with impact in global optimization
From MaRDI portal
Publication:746819
DOI10.1007/s10898-015-0278-3zbMath1353.90147OpenAlexW2004427728MaRDI QIDQ746819
Marcos Raydan, José Mario Martínez
Publication date: 20 October 2015
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10898-015-0278-3
Related Items
Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization, On High-order Model Regularization for Constrained Optimization, Large-scale unconstrained optimization using separable cubic modeling and matrix-free subspace minimization, A cubic regularization algorithm for unconstrained optimization using line search and nonmonotone techniques, On complexity and convergence of high-order coordinate descent algorithms for smooth nonconvex box-constrained minimization
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A trust region algorithm with adaptive cubic regularization methods for nonsmooth convex minimization
- Interior-point methods for nonconvex nonlinear programming: cubic regularization
- Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
- Adaptive cubic regularisation methods for unconstrained optimization. II: Worst-case function- and derivative-evaluation complexity
- Updating the regularization parameter in the adaptive cubic regularization algorithm
- Accelerating the cubic regularization of Newton's method on convex problems
- Algebraic rules for quadratic regularization of Newton's method
- Cubic regularization of Newton method and its global performance
- A subspace implementation of quasi-Newton trust region methods for unconstrained optimization
- On the use of iterative methods in cubic regularization for unconstrained optimization
- On the Evaluation Complexity of Cubic Regularization Methods for Potentially Rank-Deficient Nonlinear Least-Squares Problems and Its Relevance to Constrained Nonlinear Optimization
- Computing a Trust Region Step
- Tensor Methods for Nonlinear Equations
- Computing Optimal Locally Constrained Steps
- Tensor Methods for Unconstrained Optimization Using Second Derivatives
- A quadratic-tensor model algorithm for nonlinear least-squares problems with linear constraints
- Trust Region Methods
- Nonmonotone Spectral Projected Gradient Methods on Convex Sets
