sgld: Stochastic Gradient Langevin Dynamics (SGLD) optimization
In ngme2: Linear Latent Non-Gaussian Models with Flexible Distributions
sgld R Documentation
Stochastic Gradient Langevin Dynamics (SGLD) optimization
Description
Stochastic Gradient Langevin Dynamics (SGLD) optimization
Usage
sgld(stepsize = 0.001, temperature = 1)
Arguments
stepsize
base stepsize
temperature
non-negative Langevin temperature
Details
SGLD adds Gaussian noise to vanilla SGD updates:
x_{t+1} = x_t - \eta_t \nabla U(x_t) + \sqrt{2 T \eta_t}\,\xi_t, \quad \xi_t \sim \mathcal{N}(0, I)
where T is temperature. The implementation applies this
component-wise using the current effective stepsize.
Value
a list of control variables for optimization
(used in control_opt function)
ngme2 documentation built on May 20, 2026, 9:10 a.m.
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| sgld | R Documentation |
Stochastic Gradient Langevin Dynamics (SGLD) optimization
Description
Stochastic Gradient Langevin Dynamics (SGLD) optimization
Usage
sgld(stepsize = 0.001, temperature = 1)
Arguments
stepsize |
base stepsize |
temperature |
non-negative Langevin temperature |
Details
SGLD adds Gaussian noise to vanilla SGD updates:
x_{t+1} = x_t - \eta_t \nabla U(x_t) + \sqrt{2 T \eta_t}\,\xi_t, \quad \xi_t \sim \mathcal{N}(0, I)
where T is temperature. The implementation applies this
component-wise using the current effective stepsize.
Value
a list of control variables for optimization
(used in control_opt function)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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