scholarly journals A Globally Convergent Stabilized SQP Method

2013 ◽  
Vol 23 (4) ◽  
pp. 1983-2010 ◽  
Author(s):  
Philip E. Gill ◽  
Daniel P. Robinson
Keyword(s):  
Sqp Method ◽  
Stabilized Sqp ◽  
Globally Convergent

scholarly journals A globally convergent SQP method for semi-infinite nonlinear optimization

1988 ◽  
Vol 23 (2) ◽  
pp. 141-153 ◽  
Author(s):  
Yoshihiro Tanaka ◽  
Masao Fukushima ◽  
Toshihide Ibaraki
Keyword(s):  
Nonlinear Optimization ◽  
Sqp Method ◽  
Globally Convergent

A stabilized SQP method: global convergence

2016 ◽  
Vol 37 (1) ◽  
pp. 407-443 ◽  
Author(s):  
Philip E. Gill ◽  
Vyacheslav Kungurtsev ◽  
Daniel P. Robinson
Keyword(s):  
Global Convergence ◽  
Sqp Method ◽  
Stabilized Sqp

A stabilized SQP method: superlinear convergence

2016 ◽  
Vol 163 (1-2) ◽  
pp. 369-410 ◽  
Author(s):  
Philip E. Gill ◽  
Vyacheslav Kungurtsev ◽  
Daniel P. Robinson
Keyword(s):  
Superlinear Convergence ◽  
Sqp Method ◽  
Stabilized Sqp

scholarly journals A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Zhijun Luo ◽  
Lirong Wang
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Descent Direction ◽  
Constrained Optimization Problems ◽  
Inequality Constrained Optimization ◽  
Globally Convergent ◽  
Variable Distribution ◽  
Systems Of Linear Equations

A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.

Convexity of a discrete Carleman weighted objective functional in an inverse medium scattering problem

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Nguyen Trung Thành
Keyword(s):  
Finite Number ◽  
Scattering Problem ◽  
One Dimensional ◽  
Globally Convergent ◽  
Convergent Method ◽  
Inverse Medium ◽  
Inverse Medium Scattering ◽  
Objective Functional

AbstractWe investigate a globally convergent method for solving a one-dimensional inverse medium scattering problem using backscattering data at a finite number of frequencies. The proposed method is based on the minimization of a discrete Carleman weighted objective functional. The global convexity of this objective functional is proved.

Sign in / Sign up

Export Citation Format

Share Document