In this paper gradient and Hamiltonian dynamics are investigated in both discrete-time and sampled-data contexts. At first, the discrete gradient function is profitably employed to define discrete gradient and Hamiltonian dynamics. On these bases, it is shown that representations of these forms can be recovered when computing the sampled-data equivalent models to gradient and Hamiltonian continuous-time dynamics
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining dis-tant proposals w...
Modeling and control of port-Hamiltonian systems are extensively studied in the continuous-time lite...
Abstract: Either from a control theoretic viewpoint or from an analysis viewpoint it is necessary to...
International audienceIn this paper gradient and Hamiltonian dynamics are investigated in both discr...
This paper investigates the transformation of Hamiltonian structures under sampling. It is shown tha...
Continuous relaxations play an important role in discrete optimization, but have not seen much use i...
The process of machine learning can be considered in two stages: model selection and parameter estim...
The process of machine learning can be considered in two stages model selection and parameter estim...
AbstractDifference equations for Hamiltonian systems are derived from a discrete variational princip...
Pre-PrintThe process of machine learning can be considered in two stages model selection and paramet...
Difference equations for Hamiltonian systems are derived from a discrete variational principle. The ...
The discrete gradient methods are integrators designed to preserve invariants of ordinary differenti...
Abstract: Stochastic gradient descent is an optimisation method that combines classical gradient des...
In this paper, the differential/difference representation (DDR) of an input-affine dynamics under sa...
Either from a control theoretic viewpoint or from an analysis viewpoint it is necessary to convert s...
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining dis-tant proposals w...
Modeling and control of port-Hamiltonian systems are extensively studied in the continuous-time lite...
Abstract: Either from a control theoretic viewpoint or from an analysis viewpoint it is necessary to...
International audienceIn this paper gradient and Hamiltonian dynamics are investigated in both discr...
This paper investigates the transformation of Hamiltonian structures under sampling. It is shown tha...
Continuous relaxations play an important role in discrete optimization, but have not seen much use i...
The process of machine learning can be considered in two stages: model selection and parameter estim...
The process of machine learning can be considered in two stages model selection and parameter estim...
AbstractDifference equations for Hamiltonian systems are derived from a discrete variational princip...
Pre-PrintThe process of machine learning can be considered in two stages model selection and paramet...
Difference equations for Hamiltonian systems are derived from a discrete variational principle. The ...
The discrete gradient methods are integrators designed to preserve invariants of ordinary differenti...
Abstract: Stochastic gradient descent is an optimisation method that combines classical gradient des...
In this paper, the differential/difference representation (DDR) of an input-affine dynamics under sa...
Either from a control theoretic viewpoint or from an analysis viewpoint it is necessary to convert s...
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining dis-tant proposals w...
Modeling and control of port-Hamiltonian systems are extensively studied in the continuous-time lite...
Abstract: Either from a control theoretic viewpoint or from an analysis viewpoint it is necessary to...
DOI
Add to ORKG