Results 1 to 10 of about 62,722 (247)
Derivative-free optimization adversarial attacks for graph convolutional networks [PDF]
PeerJ Computer Science, 2021 In recent years, graph convolutional networks (GCNs) have emerged rapidly due to their excellent performance in graph data processing. However, recent researches show that GCNs are vulnerable to adversarial attacks.
Runze Yang, Teng Long
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High-dimensional normalized data profiles for testing derivative-free optimization algorithms [PDF]
PeerJ Computer Science, 2022 This article provides a new tool for examining the efficiency and robustness of derivative-free optimization algorithms based on high-dimensional normalized data profiles that test a variety of performance metrics.
Hassan Musafer +2 more
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Exploiting Problem Structure in Derivative Free Optimization [PDF]
ACM Transactions on Mathematical Software, 2022 A structured version of derivative-free random pattern search optimization algorithms is introduced, which is able to exploit coordinate partially separable structure (typically associated with sparsity) often present in unconstrained and bound-constrained optimization problems.
Porcelli M., Toint P. L.
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Hyperparameter Optimization of a Parallelized LSTM for Time Series Prediction
Vietnam Journal of Computer Science, 2023 Long Short-Term Memory (LSTM) Neural Network has great potential to predict sequential data. Time series prediction is one of the most popular experimental subjects of LSTM.
Muhammed Maruf Öztürk
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IEEE Access, 2023 The deployment of offshore platforms for the extraction of oil and gas from subsea reservoirs presents unique challenges, particularly when multiple platforms are connected by a subsea gas network.
Carlos Luguesi +4 more
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Fractal and Fractional, 2022 This paper presents a design and evaluation of a fractional-order self optimizing control (FOSOC) architecture for process control. It is based on a real-time derivative-free optimization layer that adjusts the parameters of a discrete-time fractional ...
Jairo Viola, YangQuan Chen
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Inexact Derivative-Free Optimization for Bilevel Learning [PDF]
Journal of Mathematical Imaging and Vision, 2021 AbstractVariational regularization techniques are dominant in the field of mathematical imaging. A drawback of these techniques is that they are dependent on a number of parameters which have to be set by the user. A by-now common strategy to resolve this issue is to learn these parameters from data.
Matthias J. Ehrhardt, Lindon Roberts
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On q-Quasi-Newton’s Method for Unconstrained Multiobjective Optimization Problems
Mathematics, 2020 A parameter-free optimization technique is applied in Quasi-Newton’s method for solving unconstrained multiobjective optimization problems. The components of the Hessian matrix are constructed using q-derivative, which is positive definite at every ...
Kin Keung Lai +2 more
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Novel Algorithm for Linearly Constrained Derivative Free Global Optimization of Lipschitz Functions
Mathematics, 2023 This paper introduces an innovative extension of the DIRECT algorithm specifically designed to solve global optimization problems that involve Lipschitz continuous functions subject to linear constraints.
Linas Stripinis, Remigijus Paulavičius
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Derivative-free optimization methods [PDF]
Acta Numerica, 2019 In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of a black-box or simulation oracle that does not provide derivative information. Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization.
Larson, Jeffrey +2 more
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