scholarly journals Two Modified Single-Parameter Scaling Broyden–Fletcher–Goldfarb–Shanno Algorithms for Solving Nonlinear System of Symmetric Equations

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 970
Author(s):  
Jie Guo ◽  
Zhong Wan
Keyword(s):  
Nonlinear System ◽  
Global Convergence ◽  
Line Search ◽  
Optimization Problems ◽  
The Other ◽  
Small Scale ◽  
Test Problems ◽  
Unconstrained Optimization Problems ◽  
Search Directions

In this paper, we develop two algorithms to solve a nonlinear system of symmetric equations. The first is an algorithm based on modifying two Broyden–Fletcher–Goldfarb–Shanno (BFGS) methods. One of its advantages is that it is more suitable to effectively solve a small-scale system of nonlinear symmetric equations. In contrast, the second algorithm chooses new search directions by incorporating an approximation method of computing the gradients and their difference into the determination of search directions in the first algorithm. In essence, the second one can be viewed as an extension of the conjugate gradient method recently proposed by Lv et al. for solving unconstrained optimization problems. It was proved that these search directions are sufficiently descending for the approximate residual square of the equations, independent of the used line search rules. Global convergence of the two algorithms is established under mild assumptions. To test the algorithms, they are used to solve a number of benchmark test problems. Numerical results indicate that the developed algorithms in this paper outperform the other similar algorithms available in the literature.

scholarly journals A Truncated Descent HS Conjugate Gradient Method and Its Global Convergence

2009 ◽  
Vol 2009 ◽  
pp. 1-13
Author(s):  
Wanyou Cheng ◽  
Zongguo Zhang
Keyword(s):  
Global Convergence ◽  
Line Search ◽  
Optimization Problems ◽  
Test Problems ◽  
Uniformly Convex ◽  
Convex Problems ◽  
Unconstrained Optimization Problems ◽  
Globally Convergent ◽  
Armijo Line Search ◽  
Numerical Performance

Recently, Zhang (2006) proposed a three-term modified HS (TTHS) method for unconstrained optimization problems. An attractive property of the TTHS method is that the direction generated by the method is always descent. This property is independent of the line search used. In order to obtain the global convergence of the TTHS method, Zhang proposed a truncated TTHS method. A drawback is that the numerical performance of the truncated TTHS method is not ideal. In this paper, we prove that the TTHS method with standard Armijo line search is globally convergent for uniformly convex problems. Moreover, we propose a new truncated TTHS method. Under suitable conditions, global convergence is obtained for the proposed method. Extensive numerical experiment show that the proposed method is very efficient for the test problems from the CUTE Library.

A Non-Monotone Line Search Combination Technique for Unconstrained Optimization

2014 ◽  
Vol 8 (1) ◽  
pp. 218-221 ◽  
Author(s):  
Ping Hu ◽  
Zong-yao Wang
Keyword(s):  
Global Convergence ◽  
Unconstrained Optimization ◽  
Numerical Experiments ◽  
Line Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
New Combination ◽  
Combination Rule ◽  
Combination Technique ◽  
Unconstrained Optimization Problems

We propose a non-monotone line search combination rule for unconstrained optimization problems, the corresponding non-monotone search algorithm is established and its global convergence can be proved. Finally, we use some numerical experiments to illustrate the new combination of non-monotone search algorithm’s effectiveness.

scholarly journals A randomized adaptive trust region line search method

2020 ◽  
Vol 10 (2) ◽  
pp. 259-263
Author(s):  
Saman Babaie-Kafaki ◽  
Saeed Rezaee
Keyword(s):  
Simulated Annealing ◽  
Numerical Experiments ◽  
Line Search ◽  
Optimization Problems ◽  
Trust Region ◽  
Search Method ◽  
Test Problems ◽  
Unconstrained Optimization Problems ◽  
Line Search Method ◽  
Region Line

Hybridizing the trust region, line search and simulated annealing methods, we develop a heuristic algorithm for solving unconstrained optimization problems. We make some numerical experiments on a set of CUTEr test problems to investigate efficiency of the suggested algorithm. The results show that the algorithm is practically promising.

New hyrid conjugate gradient method as a convex combination of HZ and CD methods

2021 ◽  
pp. 2150187
Author(s):  
Amira Hamdi ◽  
Badreddine Sellami ◽  
Mohammed Belloufi
Keyword(s):  
Global Convergence ◽  
Conjugate Gradient ◽  
Line Search ◽  
Optimization Problems ◽  
Convex Combination ◽  
Gradient Algorithm ◽  
Unconstrained Optimization Problems ◽  
Gradient Parameter ◽  
Sufficient Descent ◽  
Wolfe Line Search

In this paper, a new hybrid conjugate gradient algorithm is proposed for solving unconstrained optimization problems, the conjugate gradient parameter [Formula: see text] is computed as a convex combination of [Formula: see text] and [Formula: see text]. Under the wolfe line search, we prove the sufficient descent and the global convergence. Numerical results are reported to show the effectiveness of our procedure.

scholarly journals On q-BFGS algorithm for unconstrained optimization problems

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shashi Kant Mishra ◽  
Geetanjali Panda ◽  
Suvra Kanti Chakraborty ◽  
Mohammad Esmael Samei ◽  
Bhagwat Ram
Keyword(s):  
Global Convergence ◽  
Objective Function ◽  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Objective Function Value ◽  
Numerical Test ◽  
Step Length ◽  
Test Problems ◽  
Bfgs Method ◽  
Unconstrained Optimization Problems

AbstractVariants of the Newton method are very popular for solving unconstrained optimization problems. The study on global convergence of the BFGS method has also made good progress. The q-gradient reduces to its classical version when q approaches 1. In this paper, we propose a quantum-Broyden–Fletcher–Goldfarb–Shanno algorithm where the Hessian is constructed using the q-gradient and descent direction is found at each iteration. The algorithm presented in this paper is implemented by applying the independent parameter q in the Armijo–Wolfe conditions to compute the step length which guarantees that the objective function value decreases. The global convergence is established without the convexity assumption on the objective function. Further, the proposed method is verified by the numerical test problems and the results are depicted through the performance profiles.

scholarly journals A COMPARATIVE STUDY OF SOME MODIFICATIONS OF CG METHODS UNDER EXACT LINE SEARCH

2020 ◽  
Vol 1 (1) ◽  
pp. 12-17
Author(s):  
Yasir Salih ◽  
Mustafa Mamat ◽  
Sukono Sukono
Keyword(s):  
Conjugate Gradient ◽  
Numerical Experiments ◽  
Line Search ◽  
Optimization Problems ◽  
Gradient Methods ◽  
Test Problems ◽  
Convergence Properties ◽  
Exact Line Search ◽  
Unconstrained Optimization Problems ◽  
Nonlinear Unconstrained Optimization

Conjugate Gradient (CG) method is a technique used in solving nonlinear unconstrained optimization problems. In this paper, we analysed the performance of two modifications and compared the results with the classical conjugate gradient methods of. These proposed methods possesse global convergence properties for general functions using exact line search. Numerical experiments show that the two modifications are more efficient for the test problems compared to classical CG coefficients.

scholarly journals Global convergence of a modified Broyden family method for nonconvex functions

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Gonglin Yuan ◽  
Zhan Wang ◽  
Pengyuan Li
Keyword(s):  
Global Convergence ◽  
Line Search ◽  
Optimization Problems ◽  
Positive Definiteness ◽  
Inexact Line Search ◽  
Nonconvex Functions ◽  
Unconstrained Optimization Problems ◽  
Matrix Sequence ◽  
Numerical Performance ◽  
Broyden Family

<p style='text-indent:20px;'>The Broyden family method is one of the most effective methods for solving unconstrained optimization problems. However, the study of the global convergence of the Broyden family method is not sufficient. In this paper, a new Broyden family method is proposed based on the BFGS formula of Yuan and Wei (Comput. Optim. Appl. 47: 237-255, 2010). The following approaches are used in the designed algorithm: (1) a modified Broyden family formula is given, (2) every matrix sequence <inline-formula><tex-math id="M1">\begin{document}$ \{B_k\} $\end{document}</tex-math></inline-formula> generated by the new algorithm possesses positive-definiteness, and (3) the global convergence of the new presented Broyden family algorithm with the Y-W-L inexact line search is obtained for general functions. Numerical performance shows that the modified Broyden family method is competitive with the classical Broyden family method.</p>

scholarly journals Global Convergence of a New Coefficient Nonlinear Conjugate Gradient Method

2018 ◽  
Vol 11 (3) ◽  
pp. 1188
Author(s):  
Nur Syarafina Mohamed ◽  
Mustafa Mamat ◽  
Mohd Rivaie ◽  
Shazlyn Milleana Shaharuddin
Keyword(s):  
Global Convergence ◽  
Conjugate Gradient ◽  
Large Scale ◽  
Line Search ◽  
Optimization Problems ◽  
Search Direction ◽  
Nonlinear Conjugate Gradient Method ◽  
Unconstrained Optimization Problems ◽  
Nonlinear Conjugate Gradient ◽  
Large Scale Unconstrained Optimization

Nonlinear conjugate gradient (CG) methods are widely used in optimization field due to its efficiency for solving a large scale unconstrained optimization problems. Many studies and modifications have been developed in order to improve the method. The method is known to possess sufficient descend condition and its global convergence properties under strong Wolfe-Powell search direction. In this paper, the new coefficient of CG method is presented. The global convergence and sufficient descend properties of the new coefficient are established by using strong Wolfe-Powell line search direction. Results show that the new coefficient is able to globally converge under certain assumptions and theories.

Sign in / Sign up

Export Citation Format

Share Document