Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad impact. In the paper, the library skscope is introduced to overcome such an obstacle. With skscope, users can solve the SCO by just programming the objective function. The convenience of skscope is demonstrated through two examples in the paper, where sparse linear regression and trend filtering are addressed with just four lines of code. More importantly, skscope's efficient implementation allows state-of-the-art solvers to quickly attain the sparse solution regardless of the high dimensionality of parameter space. Numerical experiments reveal the available solvers in skscope can achieve up to 80x speedup on the competing relaxation solutions obtained via the benchmarked convex solver. skscope is published on the Python Package Index (PyPI) and Conda, and its source code is available at: https://github.com/abess-team/skscope.

Multiband signal is an important signal in radar and communication systems, which is generally sparse and with a wide spectrum. Due to the wide spectral range of multiband signals, traditional ADC sampling at Nyquist rate may be too difficult to achieve. Modulated wideband converter (MWC), which is based on compressed sensing (CS), is a subtle scheme to solve wide spectrum sampling problem. In addition, reconstruction algorithm is the key of MWC which can decide the performance of the architecture. In this paper, we propose an improved reconstruction method, which expands iterative support detection (ISD), an iterative recovery algorithm for one- dimensional signal, to continue to finite (CTF) system. Our algorithm uses ISD to compute the solutions which satisfy that every column's reconstructions is l1minimization. Then, utilizing the resulting matrix and support detection rule, we can find the support set and obtain the recovered signal. Our algorithm does not require the number of support set as prior knowledge. And the property of our method is close to orthogonal matching pursuit multiple measurement vectors (OMPMMV), and better than Sparsity adaptive matching pursuit multiple measurement vectors (SAMPMMV). Simulation results confirm the conclusion.