On the stopping criterion of coordinate descent method I am trying to implement the coordinate descent method to solve the dual of linear SVM problem, but blocked at the stopping criterion.

Consider the optimization problem 
\begin{equation}
\min f(\mathbf{x})
\end{equation}
under the constraints $x_i\in C_i$ for $i=1,2,\ldots,m$ where $\mathbf{x}=(x_1,x_2,\ldots,x_m)$.

At iteration $k$ we perform $m$ inner iterations where the $i$-th inner iteration updates $x_i$ by solving:
$$x_i^{k+1} = \arg\min_{y} f(x_1^{k+1},x_2^{k+1},\ldots,x_{i-1}^{k+1},y,x_{i+1}^{k},\ldots,x_{m}^{k})$$
under the constraint $y\in C_i$.

My question is, what is the stopping criterion of this algorithm if we want to obtain an $\epsilon$-accurate solution $\mathbf{x}$, i.e. $f(\mathbf{x}) - f(\mathbf{x}^*) \le \epsilon$ where $\mathbf{x}^*$ is the true optimal solution? If it's too general then let's consider in particular the dual of linear SVM problem: $$\min f(\mathbf{x}) = \frac{1}{2}\mathbf{x}^\top K \mathbf{x} - C\mathbf{1}^\top\mathbf{x},\quad \mbox{s.t. } 0\le x_i\le C \quad i=1,\ldots,m$$
where $K$ is a positive semi-definite matrix and $C$ is a positive constant.

For the moment I take $f(\mathbf{x}^k) - f(\mathbf{x}^{k+1})&lt;\epsilon$ but $f(\mathbf{x}^k) - f(\mathbf{x}^{k+1})$ is only the lower bound of $f(\mathbf{x}^k) - f(\mathbf{x}^*)$.

Thank you in advance.
Here's one possible solution:
$$\nabla f(x) = \left[ \frac{\partial f(x)}{\partial x_1} \cdots \frac{\partial f(x)}{\partial x_n}\right]^T$$
The coordinate descent algorithm is exploring all the coordinate axes, so you have an estimate of $\hat{\nabla} f_i(x_k)=\frac{\partial f(x^k)}{\partial x_i}$. In particular, when $\Big| \hat{\nabla} f_i(x_k) \Big| &lt; \epsilon \,\forall i$, then $|\nabla f(x_k)| &lt; n\epsilon$, so setting $\frac{\epsilon}{n}$ as the stopping criterion for the maximum slope along the coordinate axes is a reasonable strategy.

(This gives you guarantees about the gradient and not the actual decrease, but I believe it can be modified because your function is a convex quadratic, not sure how off the top of my head)