Parallel Optimization
Parallel Optimization is a computational strategy within optimization algorithms that belongs to the class of distributed or concurrent methods, distinguished by its ability to simultaneously evaluate multiple operations or search directions across different processing units to accelerate convergence in machine learning problems.
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988
Institutions
Publications per year
2017–2026
348
1.1K
An overview of gradient descent optimization algorithms
Sebastian Ruder · arXiv (Cornell University) · 2016 · 4.8K citations · Full text
Pymoo: Multi-Objective Optimization in Python
Julian Blank, Kalyanmoy Deb · IEEE Access · 2020 · 2.1K citations · Full text
HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
Feng Niu, Benjamin Recht, Christopher Ré et al. · arXiv (Cornell University) · 2011 · 1.2K citations · Full text
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988

University of Edinburgh
Edinburgh, United Kingdom
16 h-index
24 publications
2.2K citations
Stanford University
Stanford, United States
14 h-index
43 publications
2.9K citations

Centre National de la Recherche Scientifique
Paris, France
13 h-index
37 publications
2K citations
Carnegie Mellon University
Pittsburgh, United States
13 h-index
32 publications
2.2K citations
Dalian University of Technology
Dalian, China
12 h-index
42 publications
1.2K citations

Universidad de Málaga
Málaga, Spain
12 h-index
26 publications
1.6K citations
ETH Zurich
Zurich, Switzerland
11 h-index
22 publications
861 citations
Argonne National Laboratory
Lemont, United States
11 h-index
33 publications
895 citations
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Repository · 99 publications · 10.1K citations
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23 publications · 1.6K citations
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24 publications · 1.6K citations
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18 publications · 795 citations
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TPDS · 14 publications · 869 citations
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