Convergence of quasi-Newton matrices generated by the symmetric rank one update

1991 ◽  
Vol 50 (1-3) ◽  
pp. 177-195 ◽  
Author(s):  
A. R. Conn ◽  
N. I. M. Gould ◽  
Ph. L. Toint
Keyword(s):  
Rank One ◽  
Symmetric Rank One ◽  
Quasi Newton

scholarly journals Convergence of Symmetric Rank-One method based on Modified Quasi-Newton equation

2010 ◽  
Vol 2 (3) ◽  
Author(s):  
Farzin Modarres Khiyabani ◽  
Malik Abu Hassan ◽  
Wah June Leong
Keyword(s):  
Newton Equation ◽  
Rank One ◽  
Symmetric Rank One ◽  
Quasi Newton

scholarly journals A Symmetric Rank-One Quasi-Newton Method for Nonnegative Matrix Factorization

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Shu-Zhen Lai ◽  
Hou-Biao Li ◽  
Zu-Tao Zhang
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Hessian Matrix ◽  
Synthetic Data ◽  
Nonnegative Matrix ◽  
Matrix Approximation ◽  
Active Set ◽  
Rank One ◽  
Symmetric Rank One ◽  
Quasi Newton

As is well known, the nonnegative matrix factorization (NMF) is a dimension reduction method that has been widely used in image processing, text compressing, signal processing, and so forth. In this paper, an algorithm on nonnegative matrix approximation is proposed. This method is mainly based on a relaxed active set and the quasi-Newton type algorithm, by using the symmetric rank-one and negative curvature direction technologies to approximate the Hessian matrix. The method improves some recent results. In addition, some numerical experiments are presented in the synthetic data, imaging processing, and text clustering. By comparing with the other six nonnegative matrix approximation methods, this method is more robust in almost all cases.

Optimal Conditioning and Convergence in Rank One Quasi-Newton Updates

1988 ◽  
Vol 25 (1) ◽  
pp. 206-221 ◽  
Author(s):  
Chi Ming Ip ◽  
Michael J. Todd
Keyword(s):  
Optimal Conditioning ◽  
Rank One ◽  
Quasi Newton

Optimization With Discrete Variables Via Recursive Quadratic Programming: Part 2—Algorithm and Results

1989 ◽  
Vol 111 (1) ◽  
pp. 130-136 ◽  
Author(s):  
J. Z. Cha ◽  
R. W. Mayne
Keyword(s):  
Quadratic Programming ◽  
Performance Comparison ◽  
Programming Algorithm ◽  
Discrete Variables ◽  
Contour Analysis ◽  
Recursive Quadratic Programming ◽  
Analysis Technique ◽  
Rank One ◽  
Symmetric Rank One ◽  
Monotonicity Analysis

A discrete recursive quadratic programming algorithm is developed for a class of mixed discrete constrained nonlinear programming (MDCNP) problems. The symmetric rank one (SR1) Hessian update formula is used to generate second order information. Also, strategies, such as the watchdog technique (WT), the monotonicity analysis technique (MA), the contour analysis technique (CA), and the restoration of feasibility have been considered. Heuristic aspects of handling discrete variables are treated via the concepts and convergence discussions of Part I. This paper summarizes the details of the algorithm and its implementation. Test results for 25 different problems are presented to allow evaluation of the approach and provide a basis for performance comparison. The results show that the suggested method is a promising one, efficient and robust for the MDCNP problem.

The Symmetric Rank One Formula and its Application in Discrete Nonlinear Optimization

1988 ◽  
Author(s):  
J. Z. Cha ◽  
R. W. Mayne
Keyword(s):  
Hessian Matrix ◽  
Attractive Feature ◽  
Recursive Quadratic Programming ◽  
Rank One ◽  
Hereditary Properties ◽  
Symmetric Rank One ◽  
Derivative Information ◽  
Theoretical Considerations ◽  
Specific Search ◽  
Search Directions

Abstract The hereditary properties of the Symmetric Rank One (SRI) update formula for numerically accumulating second order derivative information are studied. The unique advantage of the SR1 formula is that it does not require specific search directions for development of the Hessian matrix. This is an attractive feature for optimization applications where arbitrary search directions may be necessary. This paper explores the use of the SR1 formula within a procedure based on recursive quadratic programming (RQP) for solving a class of mixed discrete constrained nonlinear programming (MDCNP) problems. Theoretical considerations are presented along with numerical examples which illustrate the procedure and the utility of SR1.

Sign in / Sign up

Export Citation Format

Share Document