Results 21 to 30 of about 111,305 (313)
STOCHASTIC GRADIENT METHODS FOR UNCONSTRAINED OPTIMIZATION [PDF]
Pesquisa Operacional, 2014 This papers presents an overview of gradient based methods for minimization of noisy functions. It is assumed that the objective functions is either given with error terms of stochastic nature or given as the mathematical expectation. Such problems arise in the context of simulation based optimization.
Krejić, Nataša +1 more
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Gravitational Co-evolution and Opposition-based Optimization Algorithm [PDF]
International Journal of Computational Intelligence Systems, 2013 In this paper, a Gravitational Co-evolution and Opposition-based Optimization (GCOO) algorithm is proposed for solving unconstrained optimization problems.
Yang Lou +3 more
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Regularized Nonlinear Acceleration [PDF]
, 2018 We describe a convergence acceleration technique for unconstrained optimization problems. Our scheme computes estimates of the optimum from a nonlinear average of the iterates produced by any optimization method.
Bach, Francis +2 more
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GGOPT: an unconstrained non-linear optimizer [PDF]
Computer Methods and Programs in Biomedicine, 1988 GGOPT is a derivative-free non-linear optimizer for smooth functions with added noise. If the function values arise from observations or from extensive computations, these errors can be considerable. GGOPT uses an adjustable mesh together with linear least squares to find smoothed values of the function, gradient and Hessian at the center of the mesh ...
James B. Bassingthwaighte +7 more
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Nonlinear Conjugate Gradient Methods with Wolfe Type Line Search
Abstract and Applied Analysis, 2013 Nonlinear conjugate gradient method is one of the useful methods for unconstrained optimization problems. In this paper, we consider three kinds of nonlinear conjugate gradient methods with Wolfe type line search for unstrained optimization problems ...
Yuan-Yuan Chen, Shou-Qiang Du
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An accelerated conjugate gradient method for the Z-eigenvalues of symmetric tensors
AIMS Mathematics, 2023 We transform the Z-eigenvalues of symmetric tensors into unconstrained optimization problems with a shifted parameter. An accelerated conjugate gradient method is proposed for solving these unconstrained optimization problems.
Mingyuan Cao +3 more
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A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix
Journal of Applied Mathematics, 2015 We consider the weighted low rank approximation of the positive semidefinite Hankel matrix problem arising in signal processing. By using the Vandermonde representation, we firstly transform the problem into an unconstrained optimization problem and then
Jianchao Bai +3 more
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A modified three terms PRP conjugate gradient method
Journal of Hebei University of Science and Technology, 2018 In order to effectively solve a class of large-scale unconstrained optimization problems and overcome the shortcomings of other algorithms, such as complex algorithms, large memory and computer programming difficulties, a new search direction is defined,
Songhua WANG +3 more
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Discrete Dynamics in Nature and Society, 2015 A new trust region method is presented, which combines nonmonotone line search technique, a self-adaptive update rule for the trust region radius, and the weighting technique for the ratio between the actual reduction and the predicted reduction.
Yunlong Lu +4 more
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Journal of Inequalities and Applications, 2019 In this paper, we first introduce a new algorithm which involves projecting each iteration to solve a split feasibility problem with paramonotone equilibria and using unconstrained convex optimization.
Q. L. Dong +4 more
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