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Large-Scale Data Mathematical Optimization Lab
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The lab researches algorithms that use graphs and optimization to solve problems on large-scale data quickly.
AboutAbout the lab
Designing fast algorithms for large-scale data.
The Large-Scale Data Mathematical Optimization Lab researches algorithms for large and complex data, centered on graph theory and mathematical optimization.
The lab designs fast algorithms that run in a realistic amount of time even on large-scale real-world data, and theoretically analyzes their computational complexity.
The lab also implements the algorithms it designs and verifies them.
The lab is a new lab that was established in April 2026.
Key terms
- Graph
- A mathematical structure made of points and the lines connecting them, used to represent connections such as those in a network.
- NP-hard
- A class of difficult problems for which the time needed to solve them is believed to grow very rapidly as the problem gets larger.
- Approximation algorithm
- A method that quickly finds a solution that may not be the best, but comes with a guarantee of how good it is.
- Combinatorial reconfiguration
- A field that studies procedures for changing one solution into another step by step while keeping the required conditions satisfied.
NewsLab news
View More ↗ResearchResearch themes
The lab studies both theory and applications, centered on graph theory and mathematical optimization. It designs algorithms that run in a realistic amount of time even on large-scale real-world data, and works on theoretical analysis of their computational complexity and verification through implementation.
*Illustrative imageDesign and analysis of graph algorithms
The lab develops efficient algorithms that exploit the structure of graphs, such as shortest paths, matching, and flow problems on networks.
*Illustrative imageTackling hard combinatorial optimization problems
Using approximation algorithms and parameterized algorithms, the lab explores solution methods for NP-hard combinatorial optimization problems that remain usable even at large scale.
*Illustrative imageTheory and applications of combinatorial reconfiguration
The lab analyzes the structure of transitions in the solution space, working on determining whether a sequence of transformations exists from one feasible solution to another, and on finding the shortest such sequence.
MembersFaculty
The members of the Large-Scale Data Mathematical Optimization Lab are faculty and doctoral and master's students.As of 2026
- ProfessorAkira Suzuki
- Project Assistant ProfessorKaito Ariu
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: “Network graph visualization showing bibliography and references” Ecpp / CC BY-SA 4.0 (cropped)
- Design and analysis of graph algorithms: “The topology structure of a bike-sharing network” Wu, Chunliang; Kim, Inhi / CC BY-SA 4.0 (cropped)
- Tackling hard combinatorial optimization problems: “Inland Terminal Veghel BV, Van Berkel Logistics - 01” J187B / CC BY-SA 4.0 (cropped)
- Theory and applications of combinatorial reconfiguration: “15-Puzzle solved” Arcinides / CC BY-SA 4.0 (cropped)
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