Using a spectral scaling structured BFGS method for constrained nonlinear least squares
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Publication:5379456
DOI10.1080/10556788.2017.1385073zbMath1415.90117OpenAlexW2762841836MaRDI QIDQ5379456
Publication date: 12 June 2019
Published in: Optimization Methods and Software (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10556788.2017.1385073
nonlinear programmingexact penalty methodsconstrained nonlinear least squaresspectral scaling structured BFGS method
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Cites Work
- Superlinearly convergent exact penalty methods with projected structured secant updates for constrained nonlinear least squares
- Spectral scaling BFGS method
- Augmented penalty algorithms based on BFGS secant approximations and trust regions
- Superlinearly convergent exact penalty projected structured hessian updating schemes for constrained nonlinear least squares: asymptotic analysis
- A primal-dual interior-point algorithm for nonlinear least squares constrained problems
- Operations research and optimization (ORO)
- A two-step superlinearly convergent projected structured BFGS method for constrained nonlinear least squares
- Perturbation Bounds for Determinants and Characteristic Polynomials
- Projected Hessian Updating Algorithms for Nonlinearly Constrained Optimization
- On Generating Test Problems for Nonlinear Programming Algorithms
- Second-order conditions for an exact penalty function
- Nonlinear programming via an exact penalty function: Asymptotic analysis
- Constrained nonlinear least squares: an exact penalty approach with projected structured quasi-Newton updates
- Inexact spectral projected gradient methods on convex sets
- Nonmonotone Spectral Projected Gradient Methods on Convex Sets
- Benchmarking optimization software with performance profiles.
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