A Rust-based solver for fast, embedded nonconvex parametric optimization with code generation and ROS support.
Optimization Engine (OpEn) is an open-source solver for nonconvex parametric optimization problems, designed for embedded and real-time applications. It generates fast, safe Rust code from high-level problem descriptions, enabling efficient solutions for optimal control and estimation tasks in robotics and autonomous systems.
Robotics engineers, researchers, and developers working on embedded optimization, nonlinear model predictive control, and autonomous systems requiring real-time performance.
OpEn combines the speed and safety of Rust with automatic code generation, allowing users to solve complex nonconvex problems on embedded hardware without manual solver implementation. Its ROS support and multi-language interfaces make it uniquely suited for robotics applications.
Nonconvex embedded optimization: code generation for fast real-time optimization + ROS support
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Efficiently solves complex nonconvex parametric optimization problems, as shown by real-time control at 20Hz with only 15% CPU usage on embedded devices.
Generates optimized Rust code that runs on resource-constrained embedded hardware, enabling deployment in robotics and autonomous systems without manual low-level tuning.
From high-level Python definitions using CasADi, it automatically produces safe Rust optimizer modules, reducing implementation effort and errors.
Auto-generates ROS packages for robotic systems, making it easy to integrate optimization into existing robotics workflows, as highlighted in the documentation.
Requires installation of multiple dependencies like CasADi, Rust toolchain, and specific build configurations, which can be cumbersome and error-prone for newcomers.
Primarily relies on CasADi for problem formulation, lacking direct support for other popular modeling tools like Pyomo or JuMP, which restricts flexibility.
Has a smaller community compared to established optimization libraries, leading to fewer third-party resources and potentially slower support for edge cases.