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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Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods
Algorithms, 2023 While first-order methods are popular for solving optimization problems arising in deep learning, they come with some acute deficiencies. To overcome these shortcomings, there has been recent interest in introducing second-order information through quasi-
Mahsa Yousefi, Ángeles Martínez
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Development a Hybrid Conjugate Gradient Algorithm for Solving Unconstrained Minimization Problems [PDF]
مجلة التربية والعلم, 2018 In this paper, a new hybrid nonlinear conjugate gradient method are presented, which produce sufficient descent search direction at every iteration. This method showed globally convergent under some assumptions.
Sawsan S. Ismael, Basim A. Hassan
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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
semanticscholar +1 more source
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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Diagonal quasi-Newton updating formula using log-determinant norm [PDF]
, 2015 Quasi-Newton method has been widely used in solving unconstrained optimization problems. The popularity of this method is due to the fact that only the gradient of the objective function is required at each iterate. Since second derivatives (Hessian) are
Chen, Chuei Yee +3 more
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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
semanticscholar +1 more source
Multi-Level quasi-Newton methods for the partitioned simulation of fluid-structure interaction [PDF]
, 2010 In previous work of the authors, Fourier stability analyses have been performed of Gauss-Seidel iterations between the flow solver and the structural solver in a partitioned fluid-structure interaction simulation. These analyses of the flow in an elastic
H. Wendland +5 more
core +2 more sources
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
semanticscholar +1 more source
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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