Akiko Yoshise

2.1k total citations
32 papers, 1.2k citations indexed

About

Akiko Yoshise is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Computational Mechanics. According to data from OpenAlex, Akiko Yoshise has authored 32 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Numerical Analysis, 22 papers in Computational Theory and Mathematics and 4 papers in Computational Mechanics. Recurrent topics in Akiko Yoshise's work include Advanced Optimization Algorithms Research (24 papers), Matrix Theory and Algorithms (16 papers) and Optimization and Variational Analysis (12 papers). Akiko Yoshise is often cited by papers focused on Advanced Optimization Algorithms Research (24 papers), Matrix Theory and Algorithms (16 papers) and Optimization and Variational Analysis (12 papers). Akiko Yoshise collaborates with scholars based in Japan, Israel and United States. Akiko Yoshise's co-authors include Masakazu Kojima, Shinji Mizuno, Toshihito Noma, Nimrod Megiddo, Yuichi Takano, Ryuhei Miyashiro, Toshiki Sato, Maiko Shigeno, Nguyen‐Van Thoai and Takeshi Kikuchi and has published in prestigious journals such as IEEE Access, Mathematical Programming and Annals of Operations Research.

In The Last Decade

Akiko Yoshise

31 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Akiko Yoshise Japan 13 995 932 157 149 57 32 1.2k
Levent Tunçel Canada 18 525 0.5× 603 0.6× 111 0.7× 159 1.1× 91 1.6× 75 859
Shinji Mizuno Japan 19 1.5k 1.5× 1.3k 1.4× 283 1.8× 238 1.6× 93 1.6× 53 1.7k
Mirjam Dür Germany 15 439 0.4× 459 0.5× 149 0.9× 116 0.8× 41 0.7× 33 654
Florian Jarre Germany 20 718 0.7× 713 0.8× 264 1.7× 243 1.6× 70 1.2× 54 1.2k
Yu. E. Nesterov Belgium 8 762 0.8× 707 0.8× 146 0.9× 299 2.0× 98 1.7× 12 1.0k
Masakazu Muramatsu Japan 11 455 0.5× 409 0.4× 156 1.0× 161 1.1× 51 0.9× 36 694
Clóvis C. Gonzaga Brazil 17 795 0.8× 688 0.7× 193 1.2× 213 1.4× 85 1.5× 43 993
Bahman Kalantari United States 19 583 0.6× 437 0.5× 45 0.3× 173 1.2× 66 1.2× 74 982
Robert Mifflin United States 14 914 0.9× 888 1.0× 133 0.8× 365 2.4× 81 1.4× 28 1.2k
Jean-Pierre Crouzeix France 20 777 0.8× 1.1k 1.2× 430 2.7× 96 0.6× 32 0.6× 56 1.5k

Countries citing papers authored by Akiko Yoshise

Since Specialization
Citations

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

Fields of papers citing papers by Akiko Yoshise

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akiko Yoshise

This figure shows the co-authorship network connecting the top 25 collaborators of Akiko Yoshise. A scholar is included among the top collaborators of Akiko Yoshise 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 Akiko Yoshise. Akiko Yoshise 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.
2.
Takano, Yuichi, et al.. (2021). Optimizing the Strategic Decisions for One-Way Station-Based Carsharing Systems: A Mean-CVaR Approach. IEEE Access. 9. 79816–79828. 5 indexed citations
3.
Tanaka, Akihiro & Akiko Yoshise. (2018). LP-based tractable subcones of the semidefinite plus nonnegative cone. Annals of Operations Research. 265(1). 155–182.
4.
Takagi, Hideaki, et al.. (2012). Progress in the Healthcare Service Innovation Project at the Tsukuba University Hospital. 667–671. 2 indexed citations
5.
Miyashiro, Ryuhei, et al.. (2011). The Home-Away Assignment Problems and Break Minimization/Maximization Problems in Sports Scheduling. Terrestrial Environment Research Center (University of Tsukuba). 3 indexed citations
6.
Yoshise, Akiko, et al.. (2011). A homogeneous model for mixed complementarity problems over symmetric cones. Terrestrial Environment Research Center (University of Tsukuba). 3 indexed citations
7.
Yoshise, Akiko. (2011). Homogeneous Algorithms for Monotone Complementarity Problems over Symmetric Cones. Terrestrial Environment Research Center (University of Tsukuba). 11 indexed citations
8.
Yoshise, Akiko, et al.. (2010). On optimization over the doubly nonnegative cone. Terrestrial Environment Research Center (University of Tsukuba). 13–18. 24 indexed citations
9.
Yoshise, Akiko. (2006). Interior Point Trajectories and a Homogeneous Model for Nonlinear Complementarity Problems over Symmetric Cones. SIAM Journal on Optimization. 17(4). 1129–1153. 95 indexed citations
10.
Yoshise, Akiko. (2003). A Homogeneous Model for P0 and P∗ Nonlinear Complementarity Problems. 1 indexed citations
11.
Yoshise, Akiko, et al.. (2002). A Complexity Bound of a Predictor-Corrector Smoothing Method Using CHKS-Functions for Monotone LCP. Computational Optimization and Applications. 22(3). 329–350. 1 indexed citations
12.
Yoshise, Akiko, et al.. (2000). Self-regular proximities and new search directions for nonlinear P*(K) complementarity problems. 8 indexed citations
13.
Yoshise, Akiko, et al.. (2000). A Complexity Analysis of a Smoothing Method Using CHKS-functions for Monotone Linear Complementarity Problems. Computational Optimization and Applications. 17(2-3). 183–201. 9 indexed citations
14.
Jansen, Benjamin, C. Roos, Tamás Terlaky, & Akiko Yoshise. (1997). Polynomiality of primal-dual affine scaling algorithms for nonlinear complementarity problems. Mathematical Programming. 78(3). 315–345. 11 indexed citations
15.
Kojima, Masakazu, Shinji Mizuno, & Akiko Yoshise. (1993). A little theorem of the bigℳ in interior point algorithms. Mathematical Programming. 59(1-3). 361–375. 18 indexed citations
16.
Kojima, Masakazu, Nimrod Megiddo, Toshihito Noma, & Akiko Yoshise. (1991). A unified approach to interior point algorithms for linear complementarity problems: A summary. Operations Research Letters. 10(5). 247–254. 16 indexed citations
17.
Kojima, Masakazu, Shinji Mizuno, & Akiko Yoshise. (1991). An $$O(\sqrt n L)$$ iteration potential reduction algorithm for linear complementarity problems. Mathematical Programming. 50(1-3). 331–342. 127 indexed citations
18.
Kojima, Masakazu, Shinji Mizuno, & Akiko Yoshise. (1990). Ellipsoids that contain all the solutions of a positive semi-definite linear complementarity problem. Mathematical Programming. 48(1-3). 415–435. 2 indexed citations
19.
Mizuno, Shinji, Akiko Yoshise, & Takeshi Kikuchi. (1989). PRACTICAL POLYNOMIAL TIME ALGORITHMS FOR LINEAR COMPLEMENTARITY PROBLEMS. Journal of the Operations Research Society of Japan. 32(1). 75–92. 16 indexed citations
20.
Kojima, Masakazu, Shinji Mizuno, & Akiko Yoshise. (1989). A polynomial-time algorithm for a class of linear complementarity problems. Mathematical Programming. 44(1-3). 1–26. 246 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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