# skscope: Fast Sparsity-Constrained Optimization in Python

arXiv: https://arxiv.org/abs/2403.18540 · HTML: https://mlsanity.com/paper/2403.18540/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [A Tight Bound of Hard Thresholding](https://mlsanity.com/paper/1605.01656/) (2016)
- [abess: A Fast Best Subset Selection Library in Python and R](https://mlsanity.com/paper/2110.09697/) (2021)
- [Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit](https://mlsanity.com/paper/1901.02763/) (2019)
- [Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint](https://mlsanity.com/paper/1401.0086/) (2013)
- [Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique](https://mlsanity.com/paper/2308.00251/) (2023)

## Built on by (most cited first, 1 of 1)

- [Zero-shot Concept Bottleneck Models](https://mlsanity.com/paper/2502.09018/) (2025)

## Abstract (arXiv)

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.