g^i_{prev} g^i_t  > 0                               \
      &\hspace{15mm}  \eta^i_t \leftarrow \mathrm{min}(\eta^i_{t-1}
      \eta_{+},          \Gamma_{max})
      \      &\hspace{10mm}  \textbf{else if}  \:  g^i_{prev} g^i_t <
      0                           \      &\hspace{15mm}  \eta^i_t
      \leftarrow \mathrm{max}(\eta^i_{t-1} \eta_{-},
      \Gamma_{min})
      \      &\hspace{15mm}  g^i_t \leftarrow 0
      \      &\hspace{10mm}  \textbf{else}  \:
      \      &\hspace{15mm}  \eta^i_t \leftarrow \eta^i_{t-1}
      \      &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \eta_t
      \mathrm{sign}(g_t)             \      &\hspace{5mm}g_{prev}
      \leftarrow  g_t
      \      &\rule{110mm}{0.4pt}
      \[-1.ex]      &\bf{return} \:  \theta_t
      \[-1.ex]      &\rule{110mm}{0.4pt}
      \[-1.ex] \end{aligned}
For further details regarding the algorithm we refer to the paper A
   Direct Adaptive Method for Faster Backpropagation Learning: The
   RPROP Algorithm.
Parameters: