A Dynamic Working Set Method for Compressed Sensing

We propose a dynamic working set method (DWS) for the problem $\min_{\mathtt{x} \in \mathbb{R}^n} \frac{1}{2}\|\mathtt{Ax}-\mathtt{b}\|^2 + \eta\|\mathtt{x}\|_1$ that arises from compressed sensing. DWS manages the working set while iteratively calling a regression solver to generate progressively better solutions. Our experiments show that DWS is more efficient than other state-of-the-art software in the context of compressed sensing. Scale space such that $\|b\|=1$. Let $s$ be the number of non-zeros in the unknown signal. We prove that for any given $\varepsilon > 0$, DWS reaches a solution with an additive error $\varepsilon/\eta^2$ such that each call of the solver uses only $O(\frac{1}{\varepsilon}s\log s \log\frac{1}{\varepsilon})$ variables, and each intermediate solution has $O(\frac{1}{\varepsilon}s\log s\log\frac{1}{\varepsilon})$ non-zero coordinates.