Secant penalized BFGS: a noise robust quasi-Newton method via penalizing the secant condition
From MaRDI portal
Publication:2696921
DOI10.1007/s10589-022-00448-xOpenAlexW4315703116MaRDI QIDQ2696921
Publication date: 17 April 2023
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2010.01275
quasi-Newton methodsmeasurement errorpenalty methodsleast squares estimationsecant conditionnoise robust optimization
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- A Stochastic Quasi-Newton Method for Large-Scale Optimization
- Nonsmooth optimization via quasi-Newton methods
- Computational modeling, optimization and manufacturing simulation of advanced engineering materials
- On the limited memory BFGS method for large scale optimization
- MDTri: robust and efficient global mixed integer search of spaces of multiple ternary alloys. A DIRECT-inspired optimization algorithm for experimentally accessible computational material design
- Modeling and optimization in space engineering. State of the art and new challenges
- How to catch a lion in the desert: on the solution of the coverage directed generation (CDG) problem
- CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization
- Global Convergence of Online Limited Memory BFGS
- Julia: A Fresh Approach to Numerical Computing
- Implicit Filtering
- Duality in quasi-Newton methods and new variational characterizations of the DFP and BFGS updates
- Global Convergence of a Cass of Quasi-Newton Methods on Convex Problems
- Updating the Inverse of a Matrix
- A Tool for the Analysis of Quasi-Newton Methods with Application to Unconstrained Minimization
- Algorithms for nonlinear constraints that use lagrangian functions
- Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies
- Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods
- A Limited Memory Algorithm for Bound Constrained Optimization
- A Noise-Tolerant Quasi-Newton Algorithm for Unconstrained Optimization
- Analysis of the BFGS Method with Errors
- Antenna Design by Simulation-Driven Optimization
- CUTEr and SifDec
- A Family of Variable-Metric Methods Derived by Variational Means
- A new approach to variable metric algorithms
- The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
- Conditioning of Quasi-Newton Methods for Function Minimization
- Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization