corrected rmsprop documentation

docs/tutorial/solver.md

@@ -209,18 +209,11 @@ What distinguishes the method from SGD is the weight setting $$ W $$ on which we
 The **RMSprop** (`type: "RMSProp"`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are
 
 $$
-(v_t)_i =
-\begin{cases}
-(v_{t-1})_i + \delta, &(\nabla L(W_t))_i(\nabla L(W_{t-1}))_i > 0\\
-(v_{t-1})_i \cdot (1-\delta), & \text{else}
-\end{cases}
+\operatorname{MS}((W_t)_i)= \delta\operatorname{MS}((W_{t-1})_i)+ (1-\delta)(\nabla L(W_t))_i^2 \\
+(W_{t+1})_i= (W_{t})_i -\alpha\frac{(\nabla L(W_t))_i}{\sqrt{\operatorname{MS}((W_t)_i)}}
 $$
 
-$$
-(W_{t+1})_i =(W_t)_i - \alpha (v_t)_i,
-$$
-
-If the gradient updates results in oscillations the gradient is reduced by times $$1-\delta$$. Otherwise it will be increased by $$\delta$$. The default value of $$\delta$$ (`rms_decay`) is set to $$\delta = 0.02$$.
+The default value of $$\delta$$ (`rms_decay`) is set to $$\delta=0.99$$.
 
 [1] T. Tieleman, and G. Hinton.
     [RMSProp: Divide the gradient by a running average of its recent magnitude](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).