http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
A CLASS OF NONMONOTONE SPECTRAL MEMORY GRADIENT METHOD
Zhensheng Yu,Jinsong Zang,Jingzhao Liu 대한수학회 2010 대한수학회지 Vol.47 No.1
In this paper, we develop a nonmonotone spectral memory gradient method for unconstrained optimization, where the spectral stepsize and a class of memory gradient direction are combined efficiently. The global convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show the efficiency of the proposed algorithm.
Combining trust region and linesearch algorithm for equality constrained optimization
Zhensheng Yu,Changyu Wang,Jiguo Yu 한국전산응용수학회 2004 Journal of applied mathematics & informatics Vol.14 No.-
In this paper, a combining trust region and line search algorithm for equality constrained optimization is proposed. At each iteration, we only need to solve the trust region subproblem once, when the trust region trial step can not be accepted, we switch to line search to obtain the next iteration. Hence, the difficulty of repeated solving trust region subproblem in an iterate is avoided. In order to allow the direction of negative curvature, we add second correction step in trust region step and employ nonmonotone technique in line search. The global convergence and local superlinearly rate are established under certain assumptions. Some numerical examples are given to illustrate the efficiency of the proposed algorithm.
On the global convergence of a Levenberg-Marquardt method for constrained nonlinear equations
Zhensheng Yu 한국전산응용수학회 2004 Journal of applied mathematics & informatics Vol.16 No.-
In this paper, we consider a Levenberg-Marquardt method for the solution of constrained nonlinear equation problems. The global convergence is established even without requiring the existence of an accumulation point. Some numerical tests are also presented.
COMBINING TRUST REGION AND LINESEARCH ALGORITHM FOR EQUALITY CONSTRAINED OPTIMIZATION
Yu, Zhensheng,Wang, Changyu,Yu, Jiguo 한국전산응용수학회 2004 Journal of applied mathematics & informatics Vol.14 No.1
In this paper, a combining trust region and line search algorithm for equality constrained optimization is proposed. At each iteration, we only need to solve the trust region subproblem once, when the trust region trial step can not be accepted, we switch to line search to obtain the next iteration. Hence, the difficulty of repeated solving trust region subproblem in an iterate is avoided. In order to allow the direction of negative curvature, we add second correction step in trust region step and employ nonmonotone technique in line search. The global convergence and local superlinearly rate are established under certain assumptions. Some numerical examples are given to illustrate the efficiency of the proposed algorithm.
ON THE GLOBAL CONVERGENCE OF A LEVENBERG-MARQUARDT METHOD FOR CONSTRAINED NONLINEAR EQUATIONS
Yu, Zhensheng 한국전산응용수학회 2004 Journal of applied mathematics & informatics Vol.16 No.1
In this paper, we consider a Levenberg-Marquardt method for the solution of constrained nonlinear equation problems. The global convergence is established even without requiring the existence of an accumulation point. Some numerical tests are also presented.
A CLASS OF NONMONOTONE SPECTRAL MEMORY GRADIENT METHOD
Yu, Zhensheng,Zang, Jinsong,Liu, Jingzhao Korean Mathematical Society 2010 대한수학회지 Vol.47 No.1
In this paper, we develop a nonmonotone spectral memory gradient method for unconstrained optimization, where the spectral stepsize and a class of memory gradient direction are combined efficiently. The global convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show the efficiency of the proposed algorithm.