Altro.jl Documention

Documentation for Altro.jl


ALTRO (Augmented Lagrangian TRajectory Optimizer) is a fast solver for solving nonlinear, constrained trajectory optimization problems of the form:

\[\begin{aligned} \min_{x_{0:N},u_{0:N-1}} \quad & \ell_f(x_N) + \sum_{k=0}^{N-1} \ell_k(x_k, u_k, dt) \\ \textrm{s.t.} \quad & x_{k+1} = f(x_k, u_k), \\ & g_k(x_k,u_k) \in \mathcal{K}, \\ & h_k(x_k,u_k) = 0. \end{aligned}\]

where $\mathcal{K}$ is either the negative orthant or the second-order cone.

ALTRO uses iterative LQR (iLQR) as the primary solver, which is used to generate locally-optimal linear feedback policies and satisfy the nonlinear dynamics constraints. Generic stage-wise state and control constraints are handled using an augmented Lagrangian.

Once the augmented Lagrangian solver has converged to coarse tolerances, ALTRO can switch to an active-set projected Newton phase that provides fast convergence to tight constraint satisfaction.

ALTRO has demonstrated state-of-the-art performance for convex conic MPC problems, beating SOCP solvers such as Mosek, ECOS, and SCS. For quadratic MPC problems, ALTRO has performance on-par or better than OSQP.

ALTRO builds off the interfaces provided by TrajectoryOptimization.jl and RobotDynamics.jl. Please see the documentation for those packages for a more in-depth treatment of defining dynamics models and setting up trajectory optimization problems. The purpose of this documentation is to provide insight into the ALTRO algorithm, it's Julia implementation, and the options this solver provides.

Key Features

  • State-of-the-art performance for both convex (linear dynamics) and nonlinear trajectory optimization problems
  • Convenient interface for dynamics and problem definition via TrajectoryOptimization.jl and RobotDynamics.jl.
  • Supports generic nonlinear state and control constraints at each time step.
  • Supports second-order-cone programs (SOCPs).
  • Allows initialization of both state and control trajectories.
  • Supports integration up to 4th-order Runge-Kutta methods. Higher-order methods are possible but not yet implemented.
  • Supports implicit integration schemes such as implicit midpoint.
  • Supports optimization on the space of 3D rotations.
  • Provides convenient methods for warm-starting MPC problems.
  • Provides efficient methods for auto-differentiation of costs, constraints, and dynamics via ForwardDiff.jl and



Altro.jl can be installed via the Julia package manager. Within the Julia REPL:

] # activate the package manager
(v1.5) pkg> add Altro 

A specific version can be specified using

(v1.5) pkg> add Altro@0.5

Or you can check out the main branch with

(v1.5) pkg> add Altro#main

Lastly, if you want to clone the repo into your .julia/dev/ directory for development, you can use

(v1.5) pkg> dev Altro 

This will automatically add all package dependencies (see Project.toml). If you want to explicitly use any of these dependencies (such as RobotDynamics.jl), you'll need to individually add those packages to your environment via the package manager.