Results 181 to 190 of about 146,876 (221)

Quasi-Newton Methods

2019
The Quasi-Newton methods do not compute the Hessian of nonlinear functions. The Hessian is updated by analyzing successive gradient vectors instead. The Quasi-Newton algorithm was first proposed by William C. Davidon, a physicist while working at Argonne National Laboratory, United States in 1959.
Shashi Kant Mishra, Bhagwat Ram
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A Survey of Quasi-Newton Equations and Quasi-Newton Methods for Optimization

Annals of Operations Research, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xu, Chengxian, Zhang, Jianzhong
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A Classification of Quasi-Newton Methods

Numerical Algorithms, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Quasi- Newton Methods for Nonlinear Equations

Journal of the ACM, 1968
A unified derivation is presented of the quasi-Newton methods for solving systems of nonlinear equations. The general algorithm contains, as special cases, all of the previously proposed quasi-Newton methods.
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Cancellation Errors in Quasi-Newton Methods

SIAM Journal on Scientific and Statistical Computing, 1986
Using a probabilistic estimate, the author gives the effect of cancellation on the performance of quasi-Newton methods. First, the author describes and shows that the size of the low rank correction can be measured for the BFGS method. This BFGS method is used to find a local solution \(x^*\) of the problem: minimize f(x), \(x\in {\mathbb{R}}^ n ...
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Optimization of Simulation via Quasi-Newton Methods

ORSA Journal on Computing, 1994
This paper discusses the application of quasi-Newton methods to optimization of simulation. Specifically, it describes a general methodology that combines response surface methodology and other optimization techniques with quasi-Newton methods. Using quasi-Newton methods in the vicinity of the optimum speeds up the convergence rate of response surface
Safizadeh, M. Hossein, Signorile, Robert
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Maximum Entropy Derivation of Quasi-Newton Methods

SIAM Journal on Optimization, 2016
Summary: This paper presents a maximum-entropy (MaxEnt) derivation of many commonly used quasi-Newton rules. (i) This derivation interprets the elements of the Jacobian or Hessian as means of a multivariate probability distribution; (ii) the variance is chosen to represent the uncertainty about the mean.
Waldrip, Steven H., Niven, Robert K.
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Quasi Newton methods and unconstrained optimal control problems

1986 25th IEEE Conference on Decision and Control, 1986
The authors consider the problem min F(x) on a Hilbert space H. The necessary condition \(\nabla F(x)=0\) is approximated by a sequence of finite dimensional equations \(G_ N(x)=0\), \(x\in H_ N\), which could be solved by the quasi-Newton procedure proposed by Broyden, Fletcher, Goldfarb and Shanno (BFG) [see \textit{C. G.
Kelley, C. T., Sachs, E. W.
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Quasi-Newton methods for saddlepoints

Journal of Optimization Theory and Applications, 1985
The well-known quadratically convergent methods of the Huang type [cf. \textit{H. Y. Huang}, ibid. 5, 405-423 (1970; Zbl 0184.202) and \textit{H. Y. Huang} and \textit{A. V. Levy}, ibid. 6, 269-282 (1970; Zbl 0187.404)] to maximize or minimize a function \(f: {\mathbb{R}}^ n\to {\mathbb{R}}\) are generalized to find saddlepoints of f.
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Partial-Quasi-Newton Methods

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
Chengchang Liu, Shuxian Bi, Luo Luo, John C.S. Lui   +3 more
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