Scaly: an Optimizing Compiler for Optimal Control
Abstract
We present Scaly, an open-source optimizing compiler for optimal control, written in Python, that generates fast C code. Scaly provides a modeling language embedded in Python for the parametric optimization problems that arise in model predictive control and moving horizon estimation, and computes the sparse derivatives that standard optimization solvers need. Scaly accepts general nonlinear programs but applies optimizations specific to optimal control, which preserve the repeated structure of a problem, such as the stages of a horizon, through differentiation down to the generated loops. The generated C depends only on the C math library and never allocates memory dynamically. On three benchmark problems, Scaly evaluates the Hessian of the Lagrangian up to 26 times faster than CasADi, with smaller binaries and build times that stay nearly constant with the horizon. With the same solver, Scaly shortens the solver time per control step on every benchmark, by a factor of up to 9.
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BibTeX
@misc{oancea2026scaly,
title = {{Scaly: an Optimizing Compiler for Optimal Control}},
author = {Tudor A. Oancea and Colin N. Jones},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41762},
eprint = {2610.41762},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41762},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Scaly: an Optimizing Compiler for Optimal Control
AU - Tudor A. Oancea
AU - Colin N. Jones
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.41762
UR - https://arxiv.org/abs/2610.41762
AB - We present Scaly, an open-source optimizing compiler for optimal control, written in Python, that generates fast C code. Scaly provides a modeling language embedded in Python for the parametric optimization problems that arise in model predictive control and moving horizon estimation, and computes the sparse derivatives that standard optimization solvers need. Scaly accepts general nonlinear programs but applies optimizations specific to optimal control, which preserve the repeated structure of a problem, such as the stages of a horizon, through differentiation down to the generated loops. The generated C depends only on the C math library and never allocates memory dynamically. On three benchmark problems, Scaly evaluates the Hessian of the Lagrangian up to 26 times faster than CasADi, with smaller binaries and build times that stay nearly constant with the horizon. With the same solver, Scaly shortens the solver time per control step on every benchmark, by a factor of up to 9.
ER -