skscope：Python中的快速稀疏约束优化
skscope: Fast Sparsity-Constrained Optimization in Python
AI总结：
本文介绍了Python库skscope，通过简化目标函数编程解决稀疏约束优化问题，实现高效求解并支持高维数据，相比传统方法提速达80倍。
摘要：
将迭代求解器应用于稀疏约束优化（SCO）需要繁琐的数学推导和细致的编程/调试，这阻碍了这些求解器的广泛影响。本文介绍了库skscope来克服这一障碍。借助skscope，用户只需编写目标函数即可求解SCO。本文通过两个示例展示了skscope的便捷性，其中稀疏线性回归和趋势滤波仅需四行代码即可解决。更重要的是，skscope的高效实现使得最先进的求解器能够快速获得稀疏解，而不受参数空间高维度的限制。数值实验表明，skscope中可用的求解器相较于基准凸求解器得到的竞争松弛解，可实现高达80倍的加速。skscope发布在Python Package Index (PyPI)和Conda上，其源代码可在https://github.com/abess-team/skscope获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
