Rprop
class torch.optim.Rprop(params, lr=0.01, etas=(0.5, 1.2), step_sizes=(1e-06, 50), *, foreach=None, maximize=False, differentiable=False)
Implements the resilient backpropagation algorithm.
  \begin{aligned}      &\rule{110mm}{0.4pt}
  \\      &\textbf{input}      : \theta_0 \in \mathbf{R}^d \text{
  (params)},f(\theta)          \text{ (objective)},
  \\      &\hspace{13mm}      \eta_{+/-} \text{ (etaplus,
  etaminus)}, \Gamma_{max/min}          \text{ (step sizes)}
  \\      &\textbf{initialize} :   g^0_{prev} \leftarrow 0,
  \: \eta_0 \leftarrow \text{lr (learning rate)}
  \\      &\rule{110mm}{0.4pt}
  \\      &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \:
  \textbf{do}                         \\      &\hspace{5mm}g_t
  \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
  &\hspace{5mm} \textbf{for} \text{  } i = 0, 1, \ldots, d-1 \:
  \mathbf{do}            \\      &\hspace{10mm}  \textbf{if} \:
