10:20 AM - 10:40 AM
[4P1-OS-17a-05] JijModeling: a modeler for mathematical optimization
Keywords:Mathematical Optimization, Data Science, Python, Rust
JijModeling is a Python-based mathematical optimization modeling tool that allows users to intuitively describe various optimization problems, including combinatorial optimization problems. Those problems can be solved with various solvers using our exchange format OMMX, which will be introduced in another talk.
In JijModeling, models and actual data are specified separately, making it easy to add and modify constraints, and allowing problem to be defined regardless of problem size. Since JijModeling stores constraints in an algebraic form, it achieves runtime efficiency by automatically detecting specific constraint patterns and passing them as hints to solvers when applicable.
Furthermore, by converting algebraic expressions to LaTeX, users can intuitively preview equations in Jupyter Notebook, enabling easy and intuitive model description.
In this talk, we will see the basic concepts around JijModeling, recent features, and future roadmap.
In JijModeling, models and actual data are specified separately, making it easy to add and modify constraints, and allowing problem to be defined regardless of problem size. Since JijModeling stores constraints in an algebraic form, it achieves runtime efficiency by automatically detecting specific constraint patterns and passing them as hints to solvers when applicable.
Furthermore, by converting algebraic expressions to LaTeX, users can intuitively preview equations in Jupyter Notebook, enabling easy and intuitive model description.
In this talk, we will see the basic concepts around JijModeling, recent features, and future roadmap.
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