A new structured quasi-Newton algorithm using partial information on Hessian
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Publication:966094
DOI10.1016/j.cam.2010.01.044zbMath1190.65094OpenAlexW2018506570MaRDI QIDQ966094
Ashraf Ghorbani Rizi, Keyvan Amini
Publication date: 27 April 2010
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cam.2010.01.044
superlinear convergencepartial information on the HessianDennis-More conditionmodified quasi-Newton method
Related Items (8)
Nonmonotone diagonally scaled limited-memory BFGS methods with application to compressive sensing based on a penalty model ⋮ A diagonal quasi-Newton updating method for unconstrained optimization ⋮ Global convergence of a family of modified BFGS methods under a modified weak-Wolfe-Powell line search for nonconvex functions ⋮ A NONMONOTONE ADMM-BASED DIAGONAL QUASI-NEWTON UPDATE WITH APPLICATION TO THE COMPRESSIVE SENSING PROBLEM ⋮ A Structured Quasi-Newton Algorithm for Optimizing with Incomplete Hessian Information ⋮ A modified BFGS algorithm based on a hybrid secant equation ⋮ Scaled nonlinear conjugate gradient methods for nonlinear least squares problems ⋮ Diagonally scaled memoryless quasi-Newton methods with application to compressed sensing
Cites Work
- Convergence theory for the structured BFGS secant method with an application to nonlinear least squares
- Local and superlinear convergence of quasi-Newton methods based on modified secant conditions
- Convergence Theorems for Least-Change Secant Update Methods
- Variational Methods for Non-Linear Least-Squares
- An Adaptive Nonlinear Least-Squares Algorithm
- A Characterization of Superlinear Convergence and Its Application to Quasi-Newton Methods
- Properties and numerical performance of quasi-Newton methods with modified quasi-Newton equations
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