Exploiting additional structure in equality constrained optimization by structured SQP secant algorithms
From MaRDI portal
Publication:1321339
DOI10.1007/BF00940716zbMath0792.90069OpenAlexW2036707845MaRDI QIDQ1321339
Publication date: 25 July 1994
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf00940716
Related Items (5)
A Structured Quasi-Newton Algorithm for Optimizing with Incomplete Hessian Information ⋮ Numerical algorithms for constrained maximum likelihood estimation ⋮ Operations research and optimization (ORO) ⋮ A Kantorovich theorem for the structured PSB update in Hilbert space. ⋮ Adaptive algorithm for constrained least-squares problems
Cites Work
- Convergence theory for the structured BFGS secant method with an application to nonlinear least squares
- More test examples for nonlinear programming codes
- Convergence Theorems for Least-Change Secant Update Methods
- On the Local Convergence of a Quasi-Newton Method for the Nonlinear Programming Problem
- Projected Hessian Updating Algorithms for Nonlinearly Constrained Optimization
- A Convergence Theory for a Class of Quasi-Newton Methods for Constrained Optimization
- On Secant Updates for Use in General Constrained Optimization
- Least Change Secant Updates for Quasi-Newton Methods
- An Adaptive Nonlinear Least-Squares Algorithm
- On the Local Convergence of Quasi-Newton Methods for Constrained Optimization
- Local and Superlinear Convergence for Partially Known Quasi-Newton Methods
- An SQP Augmented Lagrangian BFGS Algorithm for Constrained Optimization
- Constrained nonlinear least squares: an exact penalty approach with projected structured quasi-Newton updates
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Exploiting additional structure in equality constrained optimization by structured SQP secant algorithms
