Results 211 to 220 of about 7,757 (261)
A FAMILY OF THE QUASI-NEWTON METHODSSome of the next articles are maybe not open access.
Related searches:
Related searches:
Stochastic Quasi-Newton Methods
Proceedings of the IEEE, 2020Large-scale data science trains models for data sets containing massive numbers of samples. Training is often formulated as the solution of empirical risk minimization problems that are optimization programs whose complexity scales with the number of elements in the data set.
Aryan Mokhtari, Alejandro Ribeiro
openaire +1 more source
A Survey of Quasi-Newton Equations and Quasi-Newton Methods for Optimization
Annals of Operations Research, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chengxian Xu, Jianzhong Zhang 0001
openaire +1 more source
Approximate quasi-Newton methods
Mathematical Programming, 1990Newton-like iterative methods for nonlinear equations on Banach spaces are considered. It is proved how the local convergence behaviour of the quasi-Newton method in the infinite dimensional setting is affected by the refinement strategy. Applications to boundary value problems and integral equations are included.
C. T. Kelley, Ekkehard W. Sachs
openaire +1 more source
2021
In Chap. 6, multidimensional optimization methods were considered in which the search for the minimizer is carried out by using a set of conjugate directions. An important feature of some of these methods (e.g., the Fletcher–Reeves and Powell’s methods) is that explicit expressions for the second derivatives of \(f(\mathbf{x})\) are not required ...
Andreas Antoniou, Wu-Sheng Lu
openaire +1 more source
In Chap. 6, multidimensional optimization methods were considered in which the search for the minimizer is carried out by using a set of conjugate directions. An important feature of some of these methods (e.g., the Fletcher–Reeves and Powell’s methods) is that explicit expressions for the second derivatives of \(f(\mathbf{x})\) are not required ...
Andreas Antoniou, Wu-Sheng Lu
openaire +1 more source
2008
In this chapter we take another approach toward the development of methods lying somewhere intermediate to steepest descent and Newton’s method. Again working under the assumption that evaluation and use of the Hessian matrix is impractical or costly, the idea underlying quasi-Newton methods is to use an approximation to the inverse Hessian in place of
David G. Luenberger, Yinyu Ye
openaire +2 more sources
In this chapter we take another approach toward the development of methods lying somewhere intermediate to steepest descent and Newton’s method. Again working under the assumption that evaluation and use of the Hessian matrix is impractical or costly, the idea underlying quasi-Newton methods is to use an approximation to the inverse Hessian in place of
David G. Luenberger, Yinyu Ye
openaire +2 more sources
A Classification of Quasi-Newton Methods
Numerical Algorithms, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source