Machine Learning and Deep Learning Optimization Algorithms for Unconstrained Convex Optimization Problem

Machine Learning and Deep Learning Optimization Algorithms for Unconstrained Convex Optimization Problem

This paper conducts a thorough comparative analysis of optimization algorithms for an unconstrained convex optimization problem. It contrasts traditional methods like Gradient Descent (GD) and Nesterov Accelerated Gradient (NAG) with modern techniques such as Adaptive Moment Estimation (Adam), Long...

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Bibliographic Details
Main Authors: Kainat Naeem, Amal Bukhari, Ali Daud, Tariq Alsahfi, Bader Alshemaimri, Mousa Alhajlah
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Adaptive moment estimation (Adam)
convex optimization
deep learning (DL)
gradient descent (GD)
long short term memory (LSTM) networks
machine learning (ML)
Online Access:https://ieeexplore.ieee.org/document/10815950/
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Summary:This paper conducts a thorough comparative analysis of optimization algorithms for an unconstrained convex optimization problem. It contrasts traditional methods like Gradient Descent (GD) and Nesterov Accelerated Gradient (NAG) with modern techniques such as Adaptive Moment Estimation (Adam), Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP). Through empirical experiments, convergence speed, solution accuracy and robustness, is evaluated providing insights to aid algorithm selection. The convergence dynamics of convex optimization, is explored analyzing classical algorithms and contemporary neural network (NN) methodologies. The study concludes with a comparative assessment of these algorithms performance metrics and their respective strengths and weaknesses.
ISSN:2169-3536

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