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Yamashita Lab
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A mathematical optimization lab that finds the best answer within given constraints.
AboutAbout the lab
Mathematics that pursues the best, mathematics that serves society.
The Yamashita Lab studies mathematical optimization.
Mathematical optimization is a mathematical method for finding the best option among candidates that satisfy constraints.
The lab focuses especially on continuous optimization and nonlinear optimization, aiming to solve larger problems faster and more accurately.
It also studies a method for computing, as a semidefinite programming problem, the question of which proportions of many kinds of trees to combine to create an arboretum.
Key terms
- Mathematical optimization
- A mathematical method for finding, among the choices that satisfy given conditions, the one that is best for a given objective
- Continuous optimization
- Optimization problems in which the answer is expressed as a quantity that varies smoothly, rather than taking discrete values like a count
- Semidefinite programming problem
- A representative form of optimization problem, in which the best answer is sought under conditions that a matrix must satisfy
NewsLab news
A paper by Prof. Yamashita and colleagues was published in Numerical Algorithms
A paper by Prof. Yamashita and colleagues was published in Computational Optimization and Applications
A paper by Prof. Yamashita and colleagues was published in Annals of Operations Research
A paper by Prof. Yamashita and colleagues was published in Optimization Methods and Software
A paper by Prof. Yamashita and colleagues was published in Journal of Global Optimization
ResearchResearch themes
The lab's subject is mathematical optimization, 'a mathematical method for finding the best option among candidates that satisfy constraints.' It focuses especially on continuous optimization and nonlinear optimization, aiming to solve larger problems faster and more accurately.
*Illustrative imageShortest Delivery Routes with Drones and Ships
Drones are fast but cannot fly far; ships can travel far but are slow. The lab studies how to formulate, as a mathematical optimization problem, the route that combines the two to deliver packages in the shortest time, and how to solve it.
*Illustrative imageSolving Large Optimization Problems Quickly
In the interior-point method, the time-consuming part is solving a system of linear equations at each iteration. The lab develops methods that solve these equations quickly by deliberately allowing errors, and that control those errors well to solve large problems quickly and accurately.
MembersFaculty
The Yamashita Lab's members consist of faculty, postdoctoral researchers, and doctoral, master's, and undergraduate students.As of 2025
- ProfessorMakoto YamashitaPublications ↗
- Assistant ProfessorGodai AzumaPublications ↗
VoicesStudent voices
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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: “Rosenbrock function 2” RokerHRO / Public domain (cropped)
- Shortest Delivery Routes with Drones and Ships: “Zipline Drone Launch” Roksenhorn / CC BY-SA 4.0 (cropped)
- Solving Large Optimization Problems Quickly: “Interior-point-method-three-dimensions” User:Sdo / CC BY-SA 3.0 (cropped)
- Related lab (藤澤克樹研究室): “Automated Storage and Retrieval System” QmcBeQ3G7DZCmY84uPgT / CC BY 4.0 (cropped)
- Related lab (澄田範奈研究室): “Infeasible bipartite graph” Jj lipski / CC0 (cropped)
- Related lab (田中健一郎研究室): “Domain coloring x2-1 x-2-i x-2-i d x2+2+3i” Djiboun / CC BY-SA 4.0 (cropped)
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