# Towards end-to-end ASP computation

arXiv: https://arxiv.org/abs/2306.06821 · HTML: https://mlsanity.com/paper/2306.06821/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Enhancing Linear Algebraic Computation of Logic Programs Using Sparse Representation](https://mlsanity.com/paper/2009.10247/) (2020)

## Built on by (most cited first, 3 of 3)

- [Differentiable Logic Programming for Distant Supervision](https://mlsanity.com/paper/2408.12591/) (2024)
- [Neural Decision-Propagation for Answer Set Programming](https://mlsanity.com/paper/2605.01797/) (2026)
- [Differentiable Logic Programming to Mitigate Reasoning Shortcuts in Neurosymbolic Systems](https://mlsanity.com/paper/2607.21185/) (2026)

## Abstract (arXiv)

We propose an end-to-end approach for Answer Set Programming (ASP) and linear algebraically compute stable models satisfying given constraints. The idea is to implement Lin-Zhao's theorem together with constraints directly in vector spaces as numerical minimization of a cost function constructed from a matricized normal logic program, loop formulas in Lin-Zhao's theorem and constraints, thereby no use of symbolic ASP or SAT solvers involved in our approach. We also propose precomputation that shrinks the program size and heuristics for loop formulas to reduce computational difficulty. We empirically test our approach with programming examples including the 3-coloring and Hamiltonian cycle problems.