Two modified adaptive cubic regularization algorithms by using the nonmonotone Armijo-type line search
From MaRDI portal
Publication:6062861
DOI10.1080/02331934.2022.2075746OpenAlexW4280580405MaRDI QIDQ6062861
No author found.
Publication date: 6 November 2023
Published in: Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331934.2022.2075746
Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- An efficient nonmonotone trust-region method for unconstrained optimization
- 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
- Hybrid spectral gradient method for the unconstrained minimization problem
- Accelerating the cubic regularization of Newton's method on convex problems
- Two accelerated nonmonotone adaptive trust region line search methods
- Two nonmonotone trust region algorithms based on an improved Newton method
- On global minimizers of quadratic functions with cubic regularization
- A nonmonotone trust-region line search method for large-scale unconstrained optimization
- Incorporating nonmonotone strategies into the trust region method for unconstrained optimization
- Cubic regularization of Newton method and its global performance
- Optimization theory and methods. Nonlinear programming
- On the use of iterative methods in cubic regularization for unconstrained optimization
- Two globally convergent nonmonotone trust-region methods for unconstrained optimization
- A cubic regularization algorithm for unconstrained optimization using line search and nonmonotone techniques
- A new nonmonotone trust region method for unconstrained optimization equipped by an efficient adaptive radius
- Convergence of a Regularized Euclidean Residual Algorithm for Nonlinear Least-Squares
- Testing Unconstrained Optimization Software
- A derivative-free line search and global convergence of Broyden-like method for nonlinear equations
- An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity
- A Nonmonotone Line Search Technique and Its Application to Unconstrained Optimization
- A Nonmonotone Line Search Technique for Newton’s Method
- An Assessment of Nonmonotone Linesearch Techniques for Unconstrained Optimization
- Correction of trust region method with a new modified Newton method
- An improvement of adaptive cubic regularization method for unconstrained optimization problems
- Affine conjugate adaptive Newton methods for nonlinear elastomechanics
- A new nonmonotone line search technique for unconstrained optimization
- Benchmarking optimization software with performance profiles.
This page was built for publication: Two modified adaptive cubic regularization algorithms by using the nonmonotone Armijo-type line search
