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Yoshise Lab
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A lab that studies mathematical optimization and operations research.
AboutAbout the lab
Mathematical optimization and operations research.
The Yoshise Lab studies mathematical optimization and operations research.
Its list of research achievements includes titles such as optimization on Riemannian manifolds, conic optimization, and data collaboration analysis.
There are also titles that target real-world operations, such as nurse scheduling and dispatch planning for shared taxis.
The lab's website is built as the personal page of Professor Akiko Yoshise.
Key terms
- Mathematical optimization
- A field that uses mathematics to find the choice that best fits a given goal under specified conditions
- Operations research
- A field that analyzes operational and planning problems with mathematical methods and seeks good ways to make decisions
- Riemannian manifold
- A mathematical framework for handling curved spaces, in which distances and angles can be measured
NewsLab news
View More ↗ResearchResearch themes
The Yoshise Lab specializes in mathematical optimization and operations research. Papers and conference presentations since 2023 listed in the university's researcher directory carry titles on optimization on Riemannian manifolds, data collaboration analysis, solution methods for conic optimization, and optimization of operational planning.
*Illustrative imageOptimization on Manifolds
The lab studies methods for solving constrained optimization problems on Riemannian manifolds. Research extending interior-point methods to manifolds appears in the titles of papers and conference presentations.
*Illustrative imageData Collaboration Analysis
The lab studies methods for jointly using data held in distributed locations for machine learning while protecting privacy. Data collaboration analysis and methods using obfuscation appear in the titles of papers and conference presentations.
*Illustrative imageSolution Methods for Conic Optimization
The lab studies how to solve optimization problems known as semidefinite programming and symmetric cone programming. Its work includes research extending Chubanov's method to symmetric cones and research on post-processing algorithms for solutions.
*Illustrative imageOptimization of Operational Planning
Optimization that takes real-world operations into account appears in the titles of conference presentations, including nurse shift scheduling, dispatching of reservation-based shared taxis, and staffing of liaison personnel for university entrance exams.
MembersFaculty
- ProfessorAkiko Yoshise
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “CLR-2 logistics, Supply, transition through planning 130828-M-ZB219-309” Cpl. Paul Peterson / Public domain (cropped)
- Optimization on Manifolds: “Spaceship Earth tiles (close)” Benjamin D. Esham (bdesham) / CC BY-SA 4.0 (cropped)
- Data Collaboration Analysis: “Privacy of Devices” Stefan Nürnberger / CC BY-SA 4.0 (cropped)
- Solution Methods for Conic Optimization: “Elongated tetragonal disphenoid” Tomruen / CC BY-SA 4.0 (cropped)
- Optimization of Operational Planning: “nurse station of Hsiao Chung Cheng Hospital 20071210” Jackhsiao / Public domain (cropped)
- Related lab (Mathematical and Continuous Optimization Lab (Liu Tianxiang)): “Graph of function of 2 variables” Zasdfgbnm / Public domain (cropped)
- Related lab (Shigeno Lab): “Flight departure board at Canberra Airport, November 2022” Kgbo / CC BY-SA 4.0 (cropped)
- Related lab (Applied Probability Systems Lab): “Le torrette con i bocchettoni per il raffreddamento ad aria dei server in un data center attivo dal 2015” Lapalmauz / CC BY-SA 4.0 (cropped)
- Related lab (Arima Lab): “002 Production line - car assembly line in General Motors Manufacturing Poland - Gliwice, Poland” Marek Ślusarczyk (Tupungato) Photo portfolio / CC BY 3.0 (cropped)
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