Rescaled multilevel least-change almost secant methods

S. M. Grzegorski

doi:10.14708/ma.v19i33.1777

A family of improved secant methods via nonmonotone curvilinear paths technique for equality constrained optimization

Journal of Computational and Applied Mathematics ◽

10.1016/s0377-0427(00)00576-8 ◽

2001 ◽

Vol 136 (1-2) ◽

pp. 73-97

Author(s):

Detong Zhu

Keyword(s):

Constrained Optimization ◽

Secant Methods ◽

Equality Constrained Optimization

8. Secant Methods for Systems of Nonlinear Equations

Numerical Methods for Unconstrained Optimization and Nonlinear Equations ◽

10.1137/1.9781611971200.ch8 ◽

1996 ◽

pp. 168-193

Keyword(s):

Nonlinear Equations ◽

Systems Of Nonlinear Equations ◽

Secant Methods

Nonlinear Composites: Secant Methods and Variational Bounds

Handbook of Materials Behavior Models ◽

10.1016/b978-012443341-0/50099-5 ◽

2001 ◽

pp. 968-983 ◽

Cited By ~ 1

Author(s):

PIERRE M. SUQUET

Keyword(s):

Variational Bounds ◽

Secant Methods ◽

Nonlinear Composites

9. Secant Methods for Unconstrained Minimization

Numerical Methods for Unconstrained Optimization and Nonlinear Equations ◽

10.1137/1.9781611971200.ch9 ◽

1996 ◽

pp. 194-215

Keyword(s):

Unconstrained Minimization ◽

Secant Methods

Comparative Study of Bisection, Newton-Raphson and Secant Methods of Root- Finding Problems

IOSR Journal of Engineering ◽

10.9790/3021-04410107 ◽

2014 ◽

Vol 4 (4) ◽

pp. 01-07 ◽

Cited By ~ 3

Author(s):

Ehiwario, J.C.,

Keyword(s):

Comparative Study ◽

Root Finding ◽

Secant Methods ◽

Newton Raphson

An All-Inclusive Efficient Region of Updates for Least Change Secant Methods

SIAM Journal on Optimization ◽

10.1137/0805009 ◽

1995 ◽

Vol 5 (1) ◽

pp. 172-191 ◽

Cited By ~ 3

Author(s):

Henry Wolkowicz ◽

Qing Zhao

Keyword(s):

Secant Methods

Local Convergence of Secant Methods for Nonlinear Constrained Optimization

SIAM Journal on Numerical Analysis ◽

10.1137/0725042 ◽

1988 ◽

Vol 25 (3) ◽

pp. 692-712 ◽

Cited By ~ 26

Author(s):

Rodrigo Fontecilla

Keyword(s):

Constrained Optimization ◽

Local Convergence ◽

Nonlinear Constrained Optimization ◽

Secant Methods

Secant methods for sparse systems of nonlinear equations with a special structure

Computing ◽

10.1007/bf02253894 ◽

1983 ◽

Vol 30 (3) ◽

pp. 213-223 ◽

Cited By ~ 3

Author(s):

W. Mönch

Keyword(s):

Nonlinear Equations ◽

Special Structure ◽

Systems Of Nonlinear Equations ◽

Secant Methods ◽

Sparse Systems

Graphical representation of the application of the bisection and secant methods for obtaining roots of equations using Matlab

Journal of Physics Conference Series ◽

10.1088/1742-6596/1053/1/012026 ◽

2018 ◽

Vol 1053 ◽

pp. 012026

Author(s):

Nancy Velasco ◽

Dario Mendoza ◽

Vicente Hallo ◽

Elizabeth Salazar-Jácome ◽

Victor Chimarro

Keyword(s):

Graphical Representation ◽

Secant Methods ◽

Roots Of Equations

A family of global convergent inexact secant methods for nonconvex constrained optimization

Journal of Algorithms & Computational Technology ◽

10.1177/1748301818762497 ◽

2018 ◽

Vol 12 (2) ◽

pp. 165-176

Author(s):

Zhujun Wang ◽

Li Cai ◽

Zheng Peng

Keyword(s):

Global Convergence ◽

Constrained Optimization ◽

Line Search ◽

Merit Function ◽

Exact Penalty ◽

Search Technique ◽

Nonconvex Constrained Optimization ◽

Secant Methods ◽

Armijo Line Search ◽

Line Search Technique

We present a family of new inexact secant methods in association with Armijo line search technique for solving nonconvex constrained optimization. Different from the existing inexact secant methods, the algorithms proposed in this paper need not compute exact directions. By adopting the nonsmooth exact penalty function as the merit function, the global convergence of the proposed algorithms is established under some reasonable conditions. Some numerical results indicate that the proposed algorithms are both feasible and effective.