openscvx

Pre-release

This release is a pre-release and may not be stable for production use.

What is OpenSCvx

OpenSCvx is a general python-based successive convexification implementation which uses a JAX backend. It is designed to be easy to use for anyone and fast enough for everyone, all while being open and modular for contributors.

OpenSCvx provides a clean symbolic interface for problem definition which should be intuitive to users of NumPy, JAX, and CVXPY. This allows us to hide a lot of the under-the-hood magic away from the user while also providing a modular architecture, enabling contributors to focus on the algorithms without worrying about interface design.

OpenSCvx makes heavy use of JAX to efficiently perform calculations in the successive convex programming loop through automatic differentiation, ahead-of-time (AOT) compilation, vectorization, and GPU acceleration. Behind this is a CVXPY-based backend to solve the convex subproblems.

This is an open project and is under active development. Try it out, give us feedback, and help contribute.

import openscvx as ox

g = 9.81

# Define states
position = ox.State("position", shape=(2,))
position.min = [0.0, 0.0]
position.max = [10.0, 10.0]
position.initial = [0.0, 10.0]
position.final = [10.0, 5.0]

velocity = ox.State("velocity", shape=(1,))
velocity.min = [0.0]
velocity.max = [10.0]
velocity.initial = [0.0]
velocity.final = [ox.Free(10.0)]

# Define control (angle from vertical)
theta = ox.Control("theta", shape=(1,))
theta.min = [0.0]
theta.max = [1.755]
theta.guess = [[0.09], [1.755]]

# Define dynamics
dynamics = {
    "position": ox.Concat(
        velocity * ox.Sin(theta),
        -velocity * ox.Cos(theta),
    ),
    "velocity": g * ox.Cos(theta),
}

constraints = []
for state in [position, velocity]:
    constraints.append(ox.ctcs(state <= state.max))
    constraints.append(ox.ctcs(state.min <= state))

# Build and solve
problem = ox.Problem(
    dynamics=dynamics,
    constraints=constraints,
    states=[position, velocity],
    controls=[theta],
    time=ox.Time(initial=0.0, final=ox.Minimize(2.0), min=0.0, max=2.0),
    N=2,
)


problem.initialize()
results = problem.solve()
results = problem.post_process()

Installation

OpenSCvx is available on PyPI and can be trivially installed with pip.

It is recommended to install OpenSCvx inside a virtual environment (venv, conda, uv, etc.). If you don't already have one set up:

python3 -m venv .venv
source .venv/bin/activate

Using pip

pip install openscvx

Using uv

If you have uv installed you can prefix the commands with uv for faster installation:

uv pip install openscvx

Tip

Optional dependencies

For CVXPYGen code generation:

pip install openscvx[cvxpygen]

Tip

Nightly Builds

To install the latest development version (nightly), use the --pre flag:

pip install --pre openscvx

Installing From Source

Using pip

git clone https://github.com/OpenSCvx/OpenSCvx.git
cd OpenSCvx

python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Using uv

git clone https://github.com/OpenSCvx/OpenSCvx.git
cd OpenSCvx

uv venv
source .venv/bin/activate
uv pip install -e .

Note

MuJoCo Menagerie (optional submodule)

Core OpenSCvx installs fine from a plain clone—you do not need the submodule unless you want bundled MuJoCo Menagerie assets. To fetch it in one step, clone with --recurse-submodules, or add it later to third_party/mujoco_menagerie so openscvx.integrations.menagerie can load those MuJoCo XML/models/meshes without a separate checkout:

git clone --recurse-submodules https://github.com/OpenSCvx/OpenSCvx.git

After a clone without submodules, initialize Menagerie whenever you need it:

git submodule update --init third_party/mujoco_menagerie

Documentation

Check out the OpenSCvx documentation:

Running the Examples

We also have a selection of problems in the examples/ folder as well as on the Examples page of the documentation. The example trajectory optimization problems are grouped by application and represent some of the problem types that can be solved by OpenSCvx.

To run a problem simply run any of the examples directly, for example:

python3 examples/abstract/brachistochrone.py

and adjust the plotting as needed.

Check out the problem definitions inside examples/ to see how to define your own problems.

Code Structure

What is implemented

This repo has the following features:

Free Final Time
Fully adaptive time dilation (s is appended to the control vector)
Continuous-Time Constraint Satisfaction
FOH and ZOH exact discretization (t is a state so you can bring your own scheme)
Vectorized and Ahead-of-Time (AOT) Compiled Multishooting Discretization
JAX Autodiff for Jacobians

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Third-Party Integrations

OpenSCvx integrates with several optional third-party packages. Each installs as a pip extra (pip install openscvx[<extra>]); without it, the library and its tests degrade gracefully — the corresponding tests simply skip. Integrations with a badge are tested against main on every push and re-checked weekly against the latest upstream releases.

Integration	Extra	Provides
MuJoCo MJX	`mjx`	MJX dynamics adapter (`openscvx.integrations`)
jaxlie	`lie`	SO(3)/SE(3) Lie-group operations and IK initialization
qpax	`qpax`	JAX-native QP solver backend
CVXPYGen	`cvxpygen`	Generated C solver code for the convex subproblem
moreau	`moreau`	Licensed QP solver backend
stljax	`stl`	Signal Temporal Logic robustness bridge
frax	`frax`	Robot dynamics for the manipulator examples

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Acknowledgements

This work was supported by a NASA Space Technology Graduate Research Opportunity and the Office of Naval Research under grant N00014-17-1-2433. The authors would like to acknowledge Natalia Pavlasek, Fabio Spada, Samuel Buckner, Abhi Kamath, Govind Chari, and Purnanand Elango as well as the other Autonomous Controls Laboratory members, for their many helpful discussions and support throughout this work.

Citation

Please cite the following works if you use the repository,

@misc{hayner2026openscvxopensourcemodularextensible,
      title={OpenSCvx: An Open-Source Modular and Extensible Nonlinear Trajectory Planning Package}, 
      author={Christopher R. Hayner and Griffin J. Norris and Fabio Spada and Samet Uzun and Avi Mittal and Behcet Acıkmese and Karen Leung},
      year={2026},
      eprint={2608.21631},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2608.21631}, 
}

@article{elango2025ctcs,
title = {Continuous-time successive convexification for constrained trajectory optimization},
journal = {Automatica},
volume = {180},
pages = {112464},
year = {2025},
issn = {0005-1098},
doi = {https://doi.org/10.1016/j.automatica.2025.112464},
url = {https://www.sciencedirect.com/science/article/pii/S0005109825003589},
author = {Purnanand Elango and Dayou Luo and Abhinav G. Kamath and Samet Uzun and Taewan Kim and Behçet Açıkmeşe},
keywords = {Trajectory optimization, Optimal control, Continuous-time constraint satisfaction, Sequential convex programming},
}

@article{chari2026qoco,
  title = {QOCO: a quadratic objective conic optimizer with custom solver generation},
  author = {Chari, Govind M. and A\c{c}ıkmeşe, Beh\c{c}et},
  journal = {Mathematical Programming Computation},
  issn = {1867-2957},
  url = {http://dx.doi.org/10.1007/s12532-026-00311-8},
  doi = {10.1007/s12532-026-00311-8},
  publisher = {Springer Science and Business Media LLC},
  year = {2026},
  month = mar,
}

Additionally please run problem.citation() to generate the appropriate bibtex citations for the corresponding components used within your problem.

Metadata

Release files for openscvx 0.5.3.dev57

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