Saeed Ghadimi

19 papers receiving 1.2k citations

Saeed Ghadimi's Hit Papers

Accelerated gradient methods for nonconvex nonlinear and stochastic programming 2015 · 242 citations
2420+4+8Years since publication100200300400

Peers

Saeed Ghadimi
Comparison fields: 5 of 78
  • Numerical Analysis 248
  • Computational Mathematics 23
  • Computational Mechanics 705
  • Artificial Intelligence 942
  • Management Science and Operations Research 225
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Lin Xiao United States
Alekh Agarwal United States
Tao Pham Dinh France
Akiko Takeda Japan
Zhaoran Wang United States
Yin Tat Lee United States
Kurt M. Anstreicher United States
Ding Zhou United States
Liwei Zhang China
Jiming Peng United States
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Countries citing papers authored by Saeed Ghadimi

Since Specialization
Citations

This map shows the geographic impact of Saeed Ghadimi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Saeed Ghadimi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saeed Ghadimi more than expected).

Fields of papers citing papers by Saeed Ghadimi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Saeed Ghadimi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Saeed Ghadimi. The network helps show where Saeed Ghadimi may publish in the future.

Co-authors

The 21 scholars most cited alongside Saeed Ghadimi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Saeed Ghadimi Line = papers co-authored together Saeed Ghadimi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming
Hit paper breakdown →
2013493
2
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
Hit paper breakdown →
2015242
3 2014193
4 2012136
5 201384
6 202040
7 201129
8 201925
9 201816
10 201115
11 202211
12 20147
13 20235
14
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates
20182
15 20251
16 20241
17 20251
18 20231
19 20221
20 20240

About Saeed Ghadimi

Saeed Ghadimi is a scholar working on Artificial Intelligence, Computational Mechanics, Management Science and Operations Research, Numerical Analysis and Statistics and Probability, having authored 21 papers that have together received 1.3k indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (12 papers), Sparse and Compressive Sensing Techniques (11 papers), Risk and Portfolio Optimization (4 papers), Advanced Optimization Algorithms Research (4 papers), Advanced Bandit Algorithms Research (4 papers), Markov Chains and Monte Carlo Methods (3 papers), Advanced Statistical Methods and Models (1 paper) and Analytical Chemistry and Sensors (1 paper). The work is most often cited by research in Numerical Analysis (248 citations), Computational Mathematics (23 citations), Computational Mechanics (705 citations), Artificial Intelligence (942 citations) and Management Science and Operations Research (225 citations). Saeed Ghadimi has collaborated with scholars based in United States, Canada and Iran. Frequent co-authors include Guanghui Lan, Hongchao Zhang, Andrzej Ruszczyński, Mengdi Wang, Reza Zanjirani Farahani, Ferenc Szidarovszky, Krishnakumar Balasubramanian, Anthony Nguyen, Warren B. Powell and Parviz Norouzi. Their work appears in journals such as SIAM Journal on Optimization, Mathematical Programming, Bernoulli, Journal of Optimization Theory and Applications and IMA Journal of Management Mathematics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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