control toolbox

This toolbox intends to gather the efforts from BOCOP and Hampath project into a coherent and interoperable ensemble of high level codes.

  • ct is an ADT project at Inria Sophia in AMDT mode, developed since 2019 by members of Inria Saclay Commands team, of the APO team from Institut de Recherche en Informatique de Toulouse, and of McTAO team, with the help of Inria Sophia SED.
  • Keywords: optimal control, direct transcription, shooting, automatic differentiation, differentiable programming, interoperable codes
  • Visit the project website for download and documentation.

Hampath

Hampath is a software developed to solve optimal control problems but also to study Hamiltonian flows by a combination of shooting and differential homotopy methods. It is based on the cotcot package and uses Tapenade for automatic differentiation.

  • Hampath is developped since 2009 by members of the APO team from Institut de Recherche en Informatique de Toulouse, jointly with colleagues from the Math. Institute at Univ. Bourgogne and McTAO team at Inria Sophia.
  • Keywords: Geometric optimal control, Second order conditions, Cut and conjugate loci, Simple and Multiple shooting methods, Differential homotopy, Automatic Differentiation, Ordinary Differential Equation.
  • Visit Hampath website for download and documentation.

Cotcot

Cotcot stands for Conditions of Order Two, COnjugate Times. It is a matlab package developed to solve optimal control problems and check second order optimality conditions. It heavily relies on Fortran routines for numerical efficiency, and Tapenade for automatic differentiation. It has been developed in a joint effort of the  APO team from Institut de Recherche en Informatique de Toulouse, jointly with colleagues from the Math. Institute at Univ. Bourgogne and Orsay Math. Lab., and is now maintained by McTAO team at Inria Sophia.

qr_mumps

qr_mumps is a software package for the solution of sparse, linear systems on multicore computers. It implements a direct solution method based on the QR or Cholesky factorization of the input matrix. Therefore, it is suited to solving sparse least-squares problems, to computing the minimum-norm solution of sparse, underdetermined problems and to solving symmetric, positive-definite sparse linear systems. It can obviously be used for solving square unsymmetric problems in which case the stability provided by the use of orthogonal transformations comes at the cost of a higher operation count with respect to solvers based on, e.g., the LU factorization such as MUMPS. qr_mumps supports real and complex, single or double precision arithmetic.
For more information, visit the qr_mumps web page.