new HA2 refsol

Regression/Ex2_Regression_final_RefSol.tex

@@ -294,7 +294,7 @@ The dimension is $d=100^2 = 10000$. The gradient is obtained as
 \nabla f (\vw) = 2 \lambda \vw+  \frac{-2}{\samplesize}\sum^{\samplesize}_{\sampleidx=1}\vx^{(\sampleidx)} (y^{(\sampleidx)} - \vw^{T} \vx^{(\sampleidx)}).
 \end{equation}
 For the stopping criterion we might use a fixed number of iterations, which requires to have some understanding (``convergence analysis'') of how 
-fast gradient descent converges to the optimum. Another option is to monitor the relative decrease of the objective value $f(\vw$, i.e., 
+fast gradient descent converges to the optimum. Another option is to monitor the relative decrease of the objective value $f(\vw)$, i.e., 
 to stop iterating when $\big|\frac{f(\vw^{(k+1)}) - f(\vw^{(k)})}{f(\vw^{(k)})} \big|$ is below a suitably chosen (small) threshold. 
 \begin{figure}[h]
 \begin{center}