In this work, we present a novel approach to non-linear optimization of multivectors in the Euclidean and conformal model of geometric algebra by introducing automatic differentiation. This is used to compute gradients and Jacobian matrices of multivector valued functions for use in non-linear optimization where the emphasis is on the estimation of rigid body motions
International audienceAbstract—Simulation is ubiquitous in many scientific areas. Applied for dynami...
Automatic differentiation and nonmonotone spectral projected gradient techniques are used for solvin...
Many problems in the sciences and engineering can be rephrased as optimization problems on matrix se...
Abstract. Simulation of many physical phenomena requires the numerical solution of non-linear partia...
In this paper we present a novel method for nonlinear rigid body motion estimation from noisy data u...
AbstractWe consider a parametric linear system a(s)·x(s) = b(s) where a(s) is a regular matrix, b(s)...
The authors discuss the role of automatic differentiation tools in optimization software. We emphasi...
<p>Many algorithms for control, optimization and estimation in robotics depend on derivatives of the...
We discuss the role of automatic differentiation tools in optimization software. We emphasize issues...
AbstractWe introduce the basic notions of automatic differentiation, describe some extensions which ...
Automatic differentiation, also called computational differentiation and algorithmic differentiatio...
A large number of physical phenomena are modeled by a system of ODEs or a system of implicit ODEs. ...
Our work under this support broadly falls into five categories: automatic differentiation, sparsity,...
AbstractAdjoint mode algorithmic (also know as automatic) differentiation (AD) transforms implementa...
AbstractFor minimization problems with nonlinear equality constraints, various numerical tools are s...
International audienceAbstract—Simulation is ubiquitous in many scientific areas. Applied for dynami...
Automatic differentiation and nonmonotone spectral projected gradient techniques are used for solvin...
Many problems in the sciences and engineering can be rephrased as optimization problems on matrix se...
Abstract. Simulation of many physical phenomena requires the numerical solution of non-linear partia...
In this paper we present a novel method for nonlinear rigid body motion estimation from noisy data u...
AbstractWe consider a parametric linear system a(s)·x(s) = b(s) where a(s) is a regular matrix, b(s)...
The authors discuss the role of automatic differentiation tools in optimization software. We emphasi...
<p>Many algorithms for control, optimization and estimation in robotics depend on derivatives of the...
We discuss the role of automatic differentiation tools in optimization software. We emphasize issues...
AbstractWe introduce the basic notions of automatic differentiation, describe some extensions which ...
Automatic differentiation, also called computational differentiation and algorithmic differentiatio...
A large number of physical phenomena are modeled by a system of ODEs or a system of implicit ODEs. ...
Our work under this support broadly falls into five categories: automatic differentiation, sparsity,...
AbstractAdjoint mode algorithmic (also know as automatic) differentiation (AD) transforms implementa...
AbstractFor minimization problems with nonlinear equality constraints, various numerical tools are s...
International audienceAbstract—Simulation is ubiquitous in many scientific areas. Applied for dynami...
Automatic differentiation and nonmonotone spectral projected gradient techniques are used for solvin...
Many problems in the sciences and engineering can be rephrased as optimization problems on matrix se...