Shih-Ping Han

1.0k total citations
13 papers, 688 citations indexed

About

Shih-Ping Han is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Computational Mechanics. According to data from OpenAlex, Shih-Ping Han has authored 13 papers receiving a total of 688 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Numerical Analysis, 8 papers in Computational Theory and Mathematics and 7 papers in Computational Mechanics. Recurrent topics in Shih-Ping Han's work include Advanced Optimization Algorithms Research (13 papers), Optimization and Variational Analysis (7 papers) and Advanced Numerical Analysis Techniques (4 papers). Shih-Ping Han is often cited by papers focused on Advanced Optimization Algorithms Research (13 papers), Optimization and Variational Analysis (7 papers) and Advanced Numerical Analysis Techniques (4 papers). Shih-Ping Han collaborates with scholars based in United States and India. Shih-Ping Han's co-authors include Jong‐Shi Pang and Narayan Rangaraj and has published in prestigious journals such as Mathematical Programming, SIAM Journal on Control and Optimization and Numerische Mathematik.

In The Last Decade

Shih-Ping Han

11 papers receiving 569 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shih-Ping Han United States	8	433	396	179	157	50	13	688
V. N. Malozemov Russia	8	271 0.6×	281 0.7×	93 0.5×	106 0.7×	49 1.0×	33	545
E. S. Levitin Germany	6	388 0.9×	432 1.1×	186 1.0×	93 0.6×	80 1.6×	8	685
Jean-Pierre A. Haeberly United States	5	391 0.9×	374 0.9×	173 1.0×	74 0.5×	38 0.8×	6	528
Jan-J. Rückmann Germany	14	403 0.9×	459 1.2×	72 0.4×	206 1.3×	30 0.6×	33	683
Yu. E. Nesterov Belgium	8	762 1.8×	707 1.8×	299 1.7×	146 0.9×	98 2.0×	12	1.0k
Clóvis C. Gonzaga Brazil	17	795 1.8×	688 1.7×	213 1.2×	193 1.2×	85 1.7×	43	993
Eskil Hansen Sweden	13	268 0.6×	334 0.8×	155 0.9×	68 0.4×	65 1.3×	39	687
Soon‐Yi Wu Taiwan	18	458 1.1×	608 1.5×	183 1.0×	140 0.9×	25 0.5×	69	847
Masakazu Muramatsu Japan	11	455 1.1×	409 1.0×	161 0.9×	156 1.0×	51 1.0×	36	694

Countries citing papers authored by Shih-Ping Han

Since Specialization

Citations

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

Fields of papers citing papers by Shih-Ping Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shih-Ping Han. 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 Shih-Ping Han. The network helps show where Shih-Ping Han may publish in the future.

Co-authorship network of co-authors of Shih-Ping Han

This figure shows the co-authorship network connecting the top 25 collaborators of Shih-Ping Han. A scholar is included among the top collaborators of Shih-Ping Han 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 Shih-Ping Han. Shih-Ping Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

1.

Han, Shih-Ping, et al.. (2010). A Note on the Connection Between the Primal-Dual and the A* Algorithm. International Journal of Operations Research and Information Systems. 1(1). 73–85. 3 indexed citations

2.

Han, Shih-Ping, et al.. (2004). A Class of Methods for Projection on the Intersection of Several Ellipsoids. SIAM Journal on Optimization. 15(1). 129–138. 7 indexed citations

3.

Han, Shih-Ping, et al.. (2003). Projection algorithms in nonlinear programming. 2 indexed citations

4.

Han, Shih-Ping, et al.. (2002). On the Distance between Two Ellipsoids. SIAM Journal on Optimization. 13(1). 298–308. 47 indexed citations

5.

Han, Shih-Ping, Jong‐Shi Pang, & Narayan Rangaraj. (1992). Globally Convergent Newton Methods for Nonsmooth Equations. Mathematics of Operations Research. 17(3). 586–607. 53 indexed citations

6.

Pang, Jong‐Shi, Shih-Ping Han, & Narayan Rangaraj. (1991). Minimization of Locally Lipschitzian Functions. SIAM Journal on Optimization. 1(1). 57–82. 36 indexed citations

7.

Han, Shih-Ping. (1989). A Decomposition Method and Its Application to Convex Programming. Mathematics of Operations Research. 14(2). 237–248. 11 indexed citations

8.

Han, Shih-Ping, et al.. (1988). A parallel projection method for solving generalized linear least-squares problems. Numerische Mathematik. 53(3). 255–264. 1 indexed citations

9.

Han, Shih-Ping, et al.. (1988). A Parallel Algorithm for a Class of Convex Programs. SIAM Journal on Control and Optimization. 26(2). 345–355. 55 indexed citations

10.

Han, Shih-Ping. (1988). A successive projection method. Mathematical Programming. 40-40(1-3). 1–14. 132 indexed citations

11.

Han, Shih-Ping. (1979). Penalty Lagrangian Methods Via a Quasi-Newton Approach. Mathematics of Operations Research. 4(3). 291–302. 3 indexed citations

12.

Han, Shih-Ping. (1977). Dual Variable Metric Algorithms for Constrained Optimization. SIAM Journal on Control and Optimization. 15(4). 546–565. 35 indexed citations

13.

Han, Shih-Ping. (1976). Superlinearly convergent variable metric algorithms for general nonlinear programming problems. Mathematical Programming. 11(1). 263–282. 303 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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