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0.9.1 | Aug 14, 2024 |
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0.9.0 | Mar 20, 2024 |
0.8.1 | Oct 27, 2023 |
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0.2.2 | Mar 22, 2019 |
#2 in Robotics
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Used in optima
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Optimization Engine (OpEn) is a solver for Fast & Accurate Embedded Optimization for next-generation Robotics and Autonomous Systems.
Documentation available at alphaville.github.io/optimization-engine
Table of contents
- Features
- Demos
- Parametric optimization problems
- Code generation example
- Getting started
- Contact
- Show us your love
- Licence
- Core team
- Contributions
- Cite OpEn
Features
OpEn is the counterpart of CVXGen for nonconvex problems.
- Fast nonconvex parametric optimization
- Numerical algorithm written in Rust
- Provably safe memory management
- Auto-generation of ROS packages
OpEn is ideal for:
- Embedded Nonlinear Model Predictive Control,
- Embedded Nonlinear Moving Horizon Estimation and their applications in
- Robotics and Advanced Manufacturing Systems
- Autonomous vehicles
- Aerial Vehicles and Aerospace
Demos
Code generation
Code generation? Piece of cake!
OpEn generates parametric optimizer modules in Rust - it's blazingly fast - it's safe - it can run on embedded devices.
You can use the MATLAB or Python interface of OpEn to generate Rust code for your parametric optimizer.
This can then be called directly, using Rust, or, it can be consumed as a service over a socket.
You can generate a parametric optimizer in just very few lines of code and in no time.
OpEn allows application developers and researchers to focus on the challenges of the application, rather than the tedious task of solving the associated parametric optimization problems (as in nonlinear model predictive control).
Embedded applications
OpEn can run on embedded devices; here we see it running on an intel Atom for the autonomous navigation of a lab-scale micro aerial vehicle - the controller runs at 20Hz using only 15% CPU!
Parametric Problems
OpEn can solve nonconvex parametric optimization problems of the general form
where f is a smooth cost, U is a simple - possibly nonconvex - set, F1 and F2 are nonlinear smooth mappings and C is a convex set (read more).
Code Generation Example
Code generation in Python in just a few lines of code (read the docs for details)
import opengen as og
import casadi.casadi as cs
# Define variables
# ------------------------------------
u = cs.SX.sym("u", 5)
p = cs.SX.sym("p", 2)
# Define cost function and constraints
# ------------------------------------
phi = og.functions.rosenbrock(u, p)
f2 = cs.vertcat(1.5 * u[0] - u[1],
cs.fmax(0.0, u[2] - u[3] + 0.1))
bounds = og.constraints.Ball2(None, 1.5)
problem = og.builder.Problem(u, p, phi) \
.with_penalty_constraints(f2) \
.with_constraints(bounds)
# Configuration and code generation
# ------------------------------------
build_config = og.config.BuildConfiguration() \
.with_build_directory("python_test_build") \
.with_tcp_interface_config()
meta = og.config.OptimizerMeta()
solver_config = og.config.SolverConfiguration() \
.with_tolerance(1e-5) \
.with_constraints_tolerance(1e-4)
builder = og.builder.OpEnOptimizerBuilder(problem, meta,
build_config, solver_config)
builder.build()
Code generation in a few lines of MATLAB code (read the docs for details)
% Define variables
% ------------------------------------
u = casadi.SX.sym('u', 5);
p = casadi.SX.sym('p', 2);
% Define cost function and constraints
% ------------------------------------
phi = rosenbrock(u, p);
f2 = [1.5*u(1) - u(2);
max(0, u(3)-u(4)+0.1)];
bounds = OpEnConstraints.make_ball_at_origin(5.0);
opEnBuilder = OpEnOptimizerBuilder()...
.with_problem(u, p, phi, bounds)...
.with_build_name('penalty_new')...
.with_fpr_tolerance(1e-5)...
.with_constraints_as_penalties(f2);
opEnOptimizer = opEnBuilder.build();
Getting started
- More information about OpEn
- Quick installation guide
- OpEn in Rust
- OpEn in Python (Examples)
- OpEn in MATLAB (Examples)
- OpEn+Jupyter in Docker
- Generation of ROS packages
- Call OpEn in C/C++
- TCP/IP interface of OpEn
- Frequently asked questions
Contact us
Do you like OpEn?
Show us with a star on github...
License
OpEn is a free open source project. You can use it under the terms of either Apache license v2.0 or MIT license.
Core Team
Pantelis Sopasakis |
Emil Fresk |
Contributions
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.
Before you contribute to Optimization Engine, please read our contributing guidelines.
A list of contributors is automatically generated by github here.
Citing OpEn
Please, cite OpEn as follows (arXiv version):
@inproceedings{open2020,
author="P. Sopasakis and E. Fresk and P. Patrinos",
title="{OpEn}: Code Generation for Embedded Nonconvex Optimization",
booktitle="IFAC World Congress",
year="2020",
address="Berlin"
}
Dependencies
~2.6–4.5MB
~90K SLoC