  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.
