A New Globally Convergent Self-Scaling Vm Algorithm for Convex and Nonconvex Optimization [PDF]
Kirkuk Journal of Science, 2011 In unconstrained optimization, the original quasi-Newton condition where is the difference of the gradients at two successive iterations. Li and Fukushima proposed a modified BFGS methods based on a new Quasi –Newton equation where , where is a
Abbas Y. AL-Bayati, Basim A. Hassan
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Forward–backward quasi-Newton methods for nonsmooth optimization problems [PDF]
Computational optimization and applications, 2016 The forward–backward splitting method (FBS) for minimizing a nonsmooth composite function can be interpreted as a (variable-metric) gradient method over a continuously differentiable function which we call forward–backward envelope (FBE).
L. Stella +2 more
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Quasi-Newton methods for machine learning: forget the past, just sample [PDF]
Optim. Methods Softw., 2019 We present two sampled quasi-Newton methods (sampled LBFGS and sampled LSR1) for solving empirical risk minimization problems that arise in machine learning.
A. Berahas +3 more
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An improved quasi-Newton equation on the quasi-Newton methods for unconstrained optimizations
, 2021 Quasi-Newton methods are a class of numerical methods for solving the problem of unconstrained optimization. To improve the overall efficiency of resulting algorithms, we use the Quasi-Newton methods which is interesting for quasi-Newton equation.
Basim A. Hassan +4 more
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Methods and algorithms for determining the main quasi-homogeneous forms of polynomials and power series [PDF]
MATEC Web of Conferences, 2022 Methods are proposed that allow one to determine the special forms of polynomials and power series used in solving a number of practical problems. The most important of them are the construction of necessary and sufficient conditions for an extremum for ...
Nefedov Viktor
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Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods
Computer Vision and Pattern Recognition, 2021 In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to incorporate local curvature information.
Minghan Yang +4 more
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Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods [PDF]
SIAM Journal on Optimization, 2018 This paper presents a finite difference quasi-Newton method for the minimization of noisy functions. The method takes advantage of the scalability and power of BFGS updating, and employs an adaptive procedure for choosing the differencing interval $h ...
A. Berahas, R. Byrd, J. Nocedal
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A non-Secant quasi-Newton Method for Unconstrained Nonlinear Optimization
Cogent Engineering, 2022 The Secant equation has long been the foundation of quasi-Newton methods, as updated Hessian approximations satisfy the equation with each iteration. Several publications have lately focused on modified versions of the Secant relation, with promising ...
Issam A.R. Moghrabi
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Quasi-Newton methods for atmospheric chemistry simulations: implementation in UKCA UM vn10.8 [PDF]
Geoscientific Model Development, 2018 A key and expensive part of coupled atmospheric chemistry–climate model simulations is the integration of gas-phase chemistry, which involves dozens of species and hundreds of reactions.
E. Esentürk +12 more
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Positive Definiteness of Symmetric Rank 1 (H-Version) Update for Unconstrained Optimization
مجلة بغداد للعلوم, 2022 Several attempts have been made to modify the quasi-Newton condition in order to obtain rapid convergence with complete properties (symmetric and positive definite) of the inverse of Hessian matrix (second derivative of the objective function).
Saad Shakir Mahmood +2 more
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