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Adaptive Stochastic Gradient Descent Method for Convex and Non-Convex Optimization
Fractal and Fractional, 2022 Stochastic gradient descent is the method of choice for solving large-scale optimization problems in machine learning. However, the question of how to effectively select the step-sizes in stochastic gradient descent methods is challenging, and can ...
Ruijuan Chen, Xiaoquan Tang, Xiuting Li
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Quasi Semi and Pseudo Semi (p,E)-Convexity in Non-Linear Optimization Programming
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2023 The class of quasi semi -convex functions and pseudo semi -convex functions are presented in this paper by combining the class of -convex functions with the class of quasi semi -convex functions and pseudo semi -convex functions, respectively.
Revan I. Hazim, Saba N. Majeed
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Resource Configuration for Throughput Maximization in UAV-WPCN With Intelligent Reflecting Surface
IEEE Access, 2023 UAV-based wireless powered communication network is a promising method of power supply for battery-free IoT devices, but the limited wireless transmission capability of the UAV constrains the coverage area and transmission throughput.
Liang Xue +5 more
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Energy Informatics, 2021 Non-convex scheduling of energy production allows for more complex models that better describe the physical nature of the energy production system. Solutions to non-convex optimization problems can only be guaranteed to be local optima.
Jakob Bjørnskov +5 more
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Energy Reports, 2020 This paper presents the development of a new hybrid optimization technique termed as Immune-Commensal-Evolutionary Programming (ICEP) and its implementation to solve non-smooth/ non-convex Economic Dispatch (ED) problem.
Mohd Helmi Mansor +5 more
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Particle Swarm Optimization for Hydraulic Analysis of Water Distribution Systems [PDF]
Civil Engineering Infrastructures Journal, 2015 The analysis of flow in water-distribution networks with several pumps by the Content Model may be turned into a non-convex optimization uncertain problem with multiple solutions.
Naser Moosavian +1 more
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On some stochastic mirror descent methods for constrained online optimization problems [PDF]
Компьютерные исследования и моделирование, 2019 The problem of online convex optimization naturally occurs in cases when there is an update of statistical information. The mirror descent method is well known for non-smooth optimization problems. Mirror descent is an extension of the subgradient method
Mohammad S. Alkousa
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Characterizations of the Solution Sets of Generalized Convex Fuzzy Optimization Problem
Open Mathematics, 2019 This paper provides some new characterizations of the solution sets for non-differentiable generalized convex fuzzy optimization problem. Firstly, we introduce some new generalized convex fuzzy functions and discuss the relationships among them. Secondly,
Chen Wang, Zhou Zhiang
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Bandwidth Maximization of Disturbance Observer Based on Experimental Frequency Response Data
SICE Journal of Control, Measurement, and System Integration, 2020 A disturbance observer (DOB) has been widely employed in industrial field due to its simplicity and effectiveness in disturbance rejection. This paper focuses on systematic bandwidth-maximized DOB design by frequency response data-based convex ...
Xiaoke Wang +2 more
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Modified projected Newton scheme for non-convex function with simple constraints [PDF]
Yugoslav Journal of Operations Research, 2021 In this paper, a descent line search scheme is proposed to find a local minimum point of a non-convex optimization problem with simple constraints. The idea ensures that the scheme escapes the saddle points and finally settles for a local minimum point ...
Chakraborty Suvra Kanti +1 more
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