Optimization Problem Involving $ {L}_{2} $, $ {L}_{1} $ Norm and Constraints Can somebody suggest me how to solve the following optimization problem?
\begin{equation*}
F(\mathbf{w},\xi)=
\begin{aligned}
& \underset{\mathbf{w,\xi}}{\text{minimize}}
& & \frac{1}{2}\|\mathbf{w}-\mathbf{w}_{t-1}\|^2_2 + \beta \|\mathbf{w}\|_1 +\alpha C\xi\\
& \text{subject to}
& & 1-y\mathbf{w}^T\mathbf{x} \leq \xi, \xi \geq 0
\end{aligned}
\end{equation*}
An equivalent formulation is this:
\begin{equation*}
F(\mathbf{w},\xi)=
\begin{aligned}
& \underset{\mathbf{w,\xi}}{\text{minimize}}
& & \frac{1}{2}\|\mathbf{w}-\mathbf{w}_{t-1}\|^2_2   +\alpha C\xi\\
& \text{subject to}
& & 1-y\mathbf{w}^T\mathbf{x} \leq \xi, \xi \geq 0\\
& & &\|\mathbf{w}\|_1 \leq t
\end{aligned}
\end{equation*}
Edit: I need solution techniques that works in online setting and suitable for large $\mathbf{w,x}$, say in millions.

You can (and should) replace $\alpha C$ with $\alpha$ and $yw^Tx$ with $w^Tx$. There are many irrelevant details in your problem which can be "normalized" away.
Now, a bit of algebra reveals that it is optimal to set (indeed the problem in $\xi$ is the minimization of a line with slope $\alpha > 0$)
\begin{eqnarray}
\xi = (1 - w^Tx)_+ := \max(1-w^Tx, 0),
\end{eqnarray}
and then minimize $E(w) := \frac{1}{2}\|w-w_{t-1}\|^2 + \beta\|w\|_1 + \alpha(1 - w^Tx)_+$ for $w$.
Now, $E(w)$ can be rewritten as
\begin{eqnarray}
E(w) = g(w) + f(Kw),
\end{eqnarray}
where $K := x^T \in \mathbb{R}^{1 \times n}$, $g(w) := \frac{1}{2}\|w-w_{t-1}\|^2 + \beta\|w\|_1$, and $f(z) := \alpha(1 - z)_+$.
Thus minimizing $E(w)$ is equivalent to solving the following minimax game
\begin{eqnarray}
\underset{z \in \mathbb{R}}{\text{maximize }}\underset{w \in \mathbb{R}^n}{\text{minimize }}\langle z, Kw\rangle + g(w) - f^*(z),
\end{eqnarray}
where $f^*(z) := \underset{s \in \mathbb{R}}{\text{max }}zs - f(s)$, the convex conjugate of $f$ (compute it as exercise). It's straightforward to compute the proximal operators of $g$ (this will be a shrinkage) and $f^*$ (this will be a projection onto a compact interval followed by a translation), and so you have all the ingredients (except minor details ...) needed to invoke the proximal primal-dual algorithms of Chambolle-Pock, for example.
Finally, because $g$ is strongly convex, you will converge in $\mathcal{O}(\|x\|/\sqrt{\epsilon})$ iterations for a tolerance $\epsilon > 0$ on the duality gap.
Below are some details (useful for actually implementing the algorithms).
Computing $f^*$ and $\textrm{prox}_{\lambda f^*}$. Using basic properties of convex conjugates, we have
\begin{eqnarray}
(z)_+^* = i_{[0, 1]}(z) \implies (-z)_+^* = i_{[0,1]}(-z) = i_{[-1,0]}(z) \implies (1 - z)_+^* = z + i_{[-1,0]}(z),
\end{eqnarray}
and so $f^*(z) = \alpha(\frac{z}{\alpha}) + \alpha i_{[-1, 0]}(\frac{z}{\alpha}) = \begin{cases}z, &\mbox{ if} -\alpha \le z \le 0,\\+\infty, &\mbox{ otherwise.}\end{cases}$
A direct computation then yields that any prox step $\lambda > 0$, we have \begin{eqnarray}\textrm{prox}_{\lambda f^*}(z) = \textrm{proj}_{[\lambda - \alpha, \lambda]}(z) - \lambda,
\end{eqnarray}
i.e a projection onto a compact interval followed by a translation.
