Huifu Xu

2.6k total citations
95 papers, 1.8k citations indexed

About

Huifu Xu is a scholar working on Management Science and Operations Research, Computational Theory and Mathematics and Statistics and Probability. According to data from OpenAlex, Huifu Xu has authored 95 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Management Science and Operations Research, 38 papers in Computational Theory and Mathematics and 28 papers in Statistics and Probability. Recurrent topics in Huifu Xu's work include Risk and Portfolio Optimization (65 papers), Optimization and Variational Analysis (33 papers) and Fuzzy Systems and Optimization (27 papers). Huifu Xu is often cited by papers focused on Risk and Portfolio Optimization (65 papers), Optimization and Variational Analysis (33 papers) and Fuzzy Systems and Optimization (27 papers). Huifu Xu collaborates with scholars based in United Kingdom, China and Hong Kong. Huifu Xu's co-authors include Hailin Sun, Dali Zhang, Alexander Shapiro, Houyuan Jiang, Yongchao Liu, Victor DeMiguel, Edward Anderson, Fanwen Meng, Liwei Zhang and Daniel Ralph and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and IEEE Transactions on Automatic Control.

In The Last Decade

Huifu Xu

91 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Huifu Xu United Kingdom 25 1.1k 678 391 332 302 95 1.8k
L. Jeff Hong Hong Kong 26 1.5k 1.3× 543 0.8× 274 0.7× 319 1.0× 136 0.5× 103 2.3k
Berç Rüstem United Kingdom 26 1.3k 1.1× 490 0.7× 732 1.9× 218 0.7× 501 1.7× 121 2.7k
Rüdiger Schultz Germany 25 1.1k 1.0× 317 0.5× 706 1.8× 155 0.5× 237 0.8× 68 2.1k
András Prékopa United States 21 1.3k 1.2× 380 0.6× 796 2.0× 534 1.6× 182 0.6× 93 2.7k
Uday V. Shanbhag United States 24 757 0.7× 610 0.9× 284 0.7× 92 0.3× 404 1.3× 112 2.1k
Jerzy A. Filar Australia 22 730 0.6× 392 0.6× 260 0.7× 205 0.6× 532 1.8× 126 2.1k
François Oustry France 7 770 0.7× 534 0.8× 1.9k 4.8× 180 0.5× 122 0.4× 12 3.0k
Enriqueta Vercher Spain 20 853 0.8× 143 0.2× 211 0.5× 329 1.0× 155 0.5× 43 1.3k
Jean-Philippe Vial Switzerland 21 388 0.3× 915 1.3× 448 1.1× 106 0.3× 295 1.0× 60 2.2k
Peter Kall Switzerland 11 593 0.5× 205 0.3× 515 1.3× 178 0.5× 163 0.5× 19 1.3k

Countries citing papers authored by Huifu Xu

Since Specialization
Citations

This map shows the geographic impact of Huifu Xu'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 Huifu Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Huifu Xu more than expected).

Fields of papers citing papers by Huifu Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Huifu Xu. 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 Huifu Xu. The network helps show where Huifu Xu may publish in the future.

Co-authorship network of co-authors of Huifu Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Huifu Xu. A scholar is included among the top collaborators of Huifu Xu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Huifu Xu. Huifu Xu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sun, Hailin, et al.. (2025). Bayesian Nash Equilibrium in price competition under multinomial logit demand. European Journal of Operational Research. 324(2). 669–689. 2 indexed citations
2.
Liu, Jia, Zhiping Chen, & Huifu Xu. (2025). Preference ambiguity and robustness in multistage decision making. Mathematical Programming. 214(1-2). 847–939.
3.
Wu, Qiong, et al.. (2024). Bi-attribute utility preference robust optimization: A continuous piecewise linear approximation approach. European Journal of Operational Research. 323(1). 170–191. 2 indexed citations
4.
Xu, Huifu, et al.. (2020). Statistical robustness in utility preference robust optimization models. Mathematical Programming. 190(1-2). 679–720. 12 indexed citations
5.
Xu, Huifu, et al.. (2018). Distributionally robust shortfall risk optimization model and its approximation. Mathematical Programming. 174(1-2). 473–498. 26 indexed citations
6.
Chen, Xiaojun, Hailin Sun, & Huifu Xu. (2018). Discrete approximation of two-stage stochastic and distributionally robust linear complementarity problems. Mathematical Programming. 177(1-2). 255–289. 43 indexed citations
7.
Zhang, Jie, Huifu Xu, & Liwei Zhang. (2017). Quantitative stability analysis of stochastic quasi-variational inequality problems and applications. Mathematical Programming. 165(1). 433–470. 7 indexed citations
8.
Fliege, Jörg, et al.. (2014). Stochastic Programming with Multivariate Second Order Stochastic Dominance Constraints with Applications in Portfolio Optimization. Applied Mathematics & Optimization. 70(1). 111–140. 7 indexed citations
9.
10.
Sun, Hailin & Huifu Xu. (2011). A note on uniform exponential convergence of sample average approximation of random functions. Journal of Mathematical Analysis and Applications. 385(2). 698–708. 7 indexed citations
11.
Liu, Yongchao, Huifu Xu, & Gui-Hua Lin. (2011). Stability Analysis of One Stage Stochastic Mathematical Programs with Complementarity Constraints. Journal of Optimization Theory and Applications. 152(2). 537–555. 1 indexed citations
12.
Xu, Huifu. (2010). Uniform exponential convergence of sample average random functions under general sampling with applications in stochastic programming. Journal of Mathematical Analysis and Applications. 368(2). 692–710. 61 indexed citations
13.
Xu, Huifu & Dali Zhang. (2010). Monte Carlo methods for mean-risk optimization and portfolio selection. Computational Management Science. 9(1). 3–29. 7 indexed citations
14.
Xu, Huifu & Jane J. Ye. (2010). Approximating Stationary Points of Stochastic Mathematical Programs with Equilibrium Constraints via Sample Averaging. Set-Valued and Variational Analysis. 19(2). 283–309. 11 indexed citations
15.
Zhang, Dali, et al.. (2009). A two stage stochastic equilibrium model for electricity markets with two way contracts. Mathematical Methods of Operations Research. 71(1). 1–45. 28 indexed citations
16.
Xu, Huifu, et al.. (2008). Approximating stationary points of stochastic optimization problems in Banach space. Journal of Mathematical Analysis and Applications. 347(1). 333–343. 4 indexed citations
17.
Meng, Fanwen & Huifu Xu. (2006). Exponential convergence of sample average approximation methods for a class of stochastic mathematical programs with complementarity constraints. ePrints Soton (University of Southampton). 10 indexed citations
18.
Meng, Fanwen & Huifu Xu. (2006). A Regularized Sample Average Approximation Method for Stochastic Mathematical Programs with Nonsmooth Equality Constraints. SIAM Journal on Optimization. 17(3). 891–919. 23 indexed citations
19.
Shapiro, Alexander & Huifu Xu. (2006). Uniform laws of large numbers for set-valued mappings and subdifferentials of random functions. Journal of Mathematical Analysis and Applications. 325(2). 1390–1399. 28 indexed citations
20.
Xu, Huifu, A. M. Rubinov, & B. M. Glover. (1999). Strict lower subdifferentiability and applications. The Journal of the Australian Mathematical Society Series B Applied Mathematics. 40(3). 379–391. 3 indexed citations

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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