An Asymptotically Superlinearly Convergent Semismooth Newton Augmented Lagrangian Method for Linear Programming-Reference-Cited by-同舟云学术

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An Asymptotically Superlinearly Convergent Semismooth Newton Augmented Lagrangian Method for Linear Programming

Published:2020-01 Issue:3 Volume:30 Page:2410-2440
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Li Xudong,Sun Defeng,Toh Kim-Chuan

Funder

Shanghai Sailing Program

China Association for Science and Technology

Hong Kong Polytechnic University

National Natural Science Foundation of China

Program of Shanghai Academic Research Leader

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software

Link

https://epubs.siam.org/doi/pdf/10.1137/19M1251795

Reference33 articles.

1. Preconditioning Indefinite Systems in Interior Point Methods for Optimization

2. A family of variable metric proximal methods

3. A Variable Metric Proximal Point Algorithm for Monotone Operators

4. On the superlinear convergence of the variable metric proximal point algorithm using Broyden and BFGS matrix secant updating

5. Preconditioning and iterative solution of symmetric indefinite linear systems arising from interior point methods for linear programming

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