optimization-engine: A Rust repository from alphaville

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

Features
Demos
- Code generation
- Embedded applications
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

Contact us

Join the discussion on Discord
Reach us on Gitter

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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"
}

alphaville/optimization-engine

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