Optimization Engine (OpEn) is a solver for embedded nonconvex optimization.
Documentation available at alphaville.github.io/optimization-engine
OpEn is the counterpart of CVXGen for nonconvex problems.
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).
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!
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.
Code generation in Python in just a few lines of code (read the docs for details)
```python import opengen as og import casadi.casadi as cs
u = cs.SX.sym("u", 5) p = cs.SX.sym("p", 2)
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) \ .withpenaltyconstraints(f2) \ .with_constraints(bounds)
buildconfig = og.config.BuildConfiguration() \ .withbuilddirectory("pythontestbuild") \ .withtcpinterfaceconfig() meta = og.config.OptimizerMeta() solverconfig = og.config.SolverConfiguration() \ .withtolerance(1e-5) \ .withconstraintstolerance(1e-4) builder = og.builder.OpEnOptimizerBuilder(problem, meta, buildconfig, solverconfig) builder.build() ```
Code generation in a few lines of MATLAB code (read the docs for details)
```matlab % 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.makeballat_origin(5.0);
opEnBuilder = OpEnOptimizerBuilder()... .withproblem(u, p, phi, bounds)... .withbuildname('penaltynew')... .withfprtolerance(1e-5)... .withconstraintsas_penalties(f2);
opEnOptimizer = opEnBuilder.build(); ```
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OpEn is a free open source project. You can use it under the terms of either Apache license v2.0 or MIT license.
Pantelis Sopasakis |
Emil Fresk |
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.