Results 1 to 10 of about 35,077,595 (165)
Adaptive Restart of the Optimized Gradient Method for Convex Optimization [PDF]
Journal of Optimization Theory and Applications, 2017 First-order methods with momentum such as Nesterov's fast gradient method are very useful for convex optimization problems, but can exhibit undesirable oscillations yielding slow convergence rates for some applications.
Fessler, Jeffrey A., Kim, Donghwan
core +2 more sources
A Proximal Stochastic Gradient Method with Progressive Variance Reduction [PDF]
SIAM Journal on Optimization, 2014 We consider the problem of minimizing the sum of two convex functions: one is the average of a large number of smooth component functions, and the other is a general convex function that admits a simple proximal mapping.
Lin Xiao, Tong Zhang
exaly +2 more sources
Gradient methods with memory [PDF]
Optimization Methods and Software, 2021 In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations in order to accelerate the convergence towards the optimal solution. This information is used in the form of a piece-wise linear model of the objective function, which provides us with much better ...
Nesterov, Yurii, Florea, Mihai I.
openaire +4 more sources
Adaptive Proximal Gradient Method for Convex Optimization [PDF]
Neural Information Processing Systems, 2023 In this paper, we explore two fundamental first-order algorithms in convex optimization, namely, gradient descent (GD) and proximal gradient method (ProxGD).
Yura Malitsky, Konstantin Mishchenko
semanticscholar +1 more source
Improved step tracking algorithm based on gradient method [PDF]
MATEC Web of Conferences, 2021 The basic principle of the satcom on the move antenna is to detect the error angle between the antenna beam pointing and the satellite direction according to the received satellite signal, and convert it into the corresponding error electrical signal ...
Bei Haohan +3 more
doaj +1 more source
Policy Gradient Method For Robust Reinforcement Learning [PDF]
International Conference on Machine Learning, 2022 This paper develops the first policy gradient method with global optimality guarantee and complexity analysis for robust reinforcement learning under model mismatch.
Yue Wang, Shaofeng Zou
semanticscholar +1 more source
Fundamental investigation on free-form optimization of vibration lures
Nihon Kikai Gakkai ronbunshu, 2021 Lure fishing is a special fishing method that using artificial fishing lures as fish baits and has become more and more popular all over the world. Vibration lures are a regular item among fishing lures that perform vibration action in the water for ...
Jin-Xing SHI +2 more
doaj +1 more source
New modification of the hestenes-stiefel with strong wolfe line search [PDF]
, 2021 . The method of the nonlinear conjugate gradient is widely used in solving large-scale unconstrained optimization since been proven in solving optimization problems without using large memory storage.
Basri, Srimazzura +2 more
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Optimizing Static Linear Feedback: Gradient Method [PDF]
SIAM Journal of Control and Optimization, 2020 The linear quadratic regulator is the fundamental problem of optimal control. Its state feedback version was set and solved in the early 1960s. However static output feedback problem has no explicit-form solution.
Ilyas Fatkhullin, Boris Polyak
semanticscholar +1 more source
Sensors, 2021 This paper describes issues of leakage localization in liquid transmission pipelines. It focuses on the standard leak localization procedure, which is based on the calculation of pressure gradients using pressure measurements captured along a pipeline ...
Pawel Ostapkowicz, Andrzej Bratek
doaj +1 more source