scholarly journals Proximal Limited-Memory Quasi-Newton Methods for Scenario-based Stochastic Optimal Control * *The work of the second author was supported by the EU-funded H2020 research project DISIRE, grant agreement No. 636834. The work of the fourth author was supported by the KU Leuven Research Council under BOF/STG-15-043.

2017 ◽  
Vol 50 (1) ◽  
pp. 11865-11870
Author(s):  
Ajay Kumar Sampathirao ◽  
Pantelis Sopasakis ◽  
Alberto Bemporad ◽  
Panagiotis Patrinos
Keyword(s):  
Optimal Control ◽  
Stochastic Optimal Control ◽  
Research Council ◽  
Newton Methods ◽  
Research Project ◽  
Limited Memory ◽  
Fourth Author ◽  
Quasi Newton ◽  
The Eu

Quasi Newton Methods in Optimal Control

1985 ◽  
Vol 18 (2) ◽  
pp. 240
Author(s):  
Ekkehard W. Sachs
Keyword(s):  
Optimal Control ◽  
Newton Methods ◽  
Quasi Newton

3D magnetotelluric inversions with unstructured finite-element and limited-memory quasi-Newton methods

2018 ◽  
Vol 15 (3-4) ◽  
pp. 556-565
Author(s):  
Xiao-Yue Cao ◽  
Chang-Chun Yin ◽  
Bo Zhang ◽  
Xin Huang ◽  
Yun-He Liu ◽  
...  
Keyword(s):  
Finite Element ◽  
Newton Methods ◽  
Limited Memory ◽  
Quasi Newton

Numerical Experience with Limited-Memory Quasi-Newton and Truncated Newton Methods

1993 ◽  
Vol 3 (3) ◽  
pp. 582-608 ◽  
Author(s):  
X. Zou ◽  
I. M. Navon ◽  
M. Berger ◽  
K. H. Phua ◽  
T. Schlick ◽  
...  
Keyword(s):  
Newton Methods ◽  
Numerical Experience ◽  
Limited Memory ◽  
Truncated Newton Methods ◽  
Truncated Newton ◽  
Quasi Newton

scholarly journals On preconditioner updates for sequences of saddle-point linear systems

2018 ◽  
Vol 9 (1) ◽  
pp. 35-41
Author(s):  
Valentina De Simone ◽  
Daniela di Serafino ◽  
Benedetta Morini
Keyword(s):  
Saddle Point ◽  
Linear Systems ◽  
Solution Process ◽  
Newton Methods ◽  
Krylov Methods ◽  
Limited Memory ◽  
Short Survey ◽  
The Cost ◽  
Quasi Newton

Abstract Updating preconditioners for the solution of sequences of large and sparse saddle- point linear systems via Krylov methods has received increasing attention in the last few years, because it allows to reduce the cost of preconditioning while keeping the efficiency of the overall solution process. This paper provides a short survey of the two approaches proposed in the literature for this problem: updating the factors of a preconditioner available in a block LDLT form, and updating a preconditioner via a limited-memory technique inspired by quasi-Newton methods.

scholarly journals A dense initialization for limited-memory quasi-Newton methods

2019 ◽  
Vol 74 (1) ◽  
pp. 121-142 ◽  
Author(s):  
Johannes Brust ◽  
Oleg Burdakov ◽  
Jennifer B. Erway ◽  
Roummel F. Marcia
Keyword(s):  
Newton Methods ◽  
Limited Memory ◽  
Quasi Newton
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