Cubic regularization in symmetric rank-1 quasi-Newton methods
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Publication:1741108
DOI10.1007/s12532-018-0136-7zbMath1411.90315OpenAlexW2793277954MaRDI QIDQ1741108
David F. Shanno, Hande Y. Benson
Publication date: 3 May 2019
Published in: Mathematical Programming Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s12532-018-0136-7
Nonlinear programming (90C30) Methods of quasi-Newton type (90C53) Computational methods for problems pertaining to operations research and mathematical programming (90-08)
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Cites Work
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- A Modeling Language for Mathematical Programming
- f2c
- A trust region algorithm with adaptive cubic regularization methods for nonsmooth convex minimization
- Interior-point methods for nonconvex nonlinear programming: cubic regularization
- Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models
- 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
- Convergence of quasi-Newton matrices generated by the symmetric rank one update
- An interior-point algorithm for nonconvex nonlinear programming
- Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization
- Cubic regularization of Newton method and its global performance
- On the use of iterative methods in cubic regularization for unconstrained optimization
- Unified approach to quadratically convergent algorithms for function minimization
- Global convergence analysis of line search interior-point methods for nonlinear programming without regularity assumptions
- A cubic regularization algorithm for unconstrained optimization using line search and nonmonotone techniques
- Cubic overestimation and secant updating for unconstrained optimization ofC2, 1functions
- Variable Metric Method for Minimization
- Self-Scaling Variable Metric (SSVM) Algorithms
- Self-Scaling Variable Metric (SSVM) Algorithms
- Optimal conditioning of self-scaling variable Metric algorithms
- Matrix conditioning and nonlinear optimization
- Conjugate Gradient Methods with Inexact Searches
- Numerical Optimization
- CUTE
- A Theoretical and Experimental Study of the Symmetric Rank-One Update
- Analysis of a Symmetric Rank-One Trust Region Method
- A Rapidly Convergent Descent Method for Minimization
- Affine conjugate adaptive Newton methods for nonlinear elastomechanics
- Quasi-Newton Methods and their Application to Function Minimisation
- Variance algorithm for minimization
- Convergence Conditions for Ascent Methods
- A Family of Variable-Metric Methods Derived by Variational Means
- The Convergence of a Class of Double-rank Minimization Algorithms
- A new approach to variable metric algorithms
- Convergence Conditions for Ascent Methods. II: Some Corrections
- Conditioning of Quasi-Newton Methods for Function Minimization
- Recent advances in unconstrained optimization
- Benchmarking optimization software with performance profiles.
