#solver #multi-objective #maxsat #algorithm

app scuttle

A multi-objective MaxSAT solver

5 releases (3 breaking)

new 0.4.0 May 1, 2025
0.3.1 Aug 7, 2024
0.3.0 Feb 23, 2024
0.2.0 Sep 5, 2023
0.1.0 Jul 12, 2023

#120 in Algorithms

49 downloads per month

MIT license

2MB
8K SLoC

Scuttle - A Multi-Objective MaxSAT Solver in Rust

Scuttle is a multi-objective MaxSAT solver written in Rust and based on the RustSAT library and the CaDiCaL SAT solver.

Publications

This solver was used in the following publications. For each publication, a tag (specified in brackets) marks the exact revision used:

  • CP'23 (cp23): "Preprocessing in SAT-Based Multi-Objective Combinatorial Optimization" [5]. Additional material here.
  • CPAIOR'24 (cpaior24): "Core Boosting in SAT-Based Multi-Objective Optimization" [6]. Additional material in cpaior24/.
  • TACAS'25 (tacas25): "Certifying Pareto-Optimality in Multi-Objective Maximum Satisfiability" [7]. Additional material in tacas25/.

Algorithms

First argument Description
p-minimal P-Minimal model enumeration as described in [1] and [2]
lower-bounding Lower-bounding search as described in [3] (called "core-guided" there)
bioptsat Sat-Unsat variant of the BiOptSat algorithm described in [4]

Building

Note: Scuttle requires nightly Rust, which can be installed via rustup.

If you simply want a binary of the solver, you can install it from crates.io by running cargo +nightly install --locked scuttle.

To build the project from source, make sure to initialize the git submodules with git submodule update --init --recursive. You can then build scuttle by running cargo +nightly build.

By default, MaxPre preprocessing is not included in the build anymore. To include preprocessing with MaxPre, add --features=maxpre.

Features

  • sol-tightening: includes heuristic tightening of solutions after they are found in the build
  • maxpre: includes preprocessing with MaxPre in the build

What's The Name

Apparently "scuttle" is one of multiple term for a group of crabs, which seemed fitting for a multi-objective solver in Rust.

References

  • Takehide Soh and Mutsunori Banbara and Naoyuki Tamura and Daniel Le Berre: Solving Multiobjective Discrete Optimization Problems with Propositional Minimal Model Generation, CP 2017.
  • Miyuki Koshimura and Hidetomo Nabeshima and Hiroshi Fujita and Ryuzo Hasegawa: Minimal Model Generation with Respect to an Atom Set, FTP 2009.
  • Joao Cortes and Ines Lynce and Vasco M. Maquinho: New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization, TACAS 2023.
  • Christoph Jabs and Jeremias Berg and Andreas Niskanen and Matti Järvisalo: MaxSAT-Based Bi-Objective Boolean Optimization, SAT 2022.
  • Christoph Jabs and Jeremias Berg and Hannes Ihalainen and Matti Järvisalo: Preprocessing in SAT-Based Multi-Objective Combinatorial Optimization, CP 2023.
  • Christoph Jabs and Jeremias Berg and Matti Järvisalo: Core Boosting in SAT-Based Multi-Objective Optimization, CPAIOR 2024.
  • Christoph Jabs and Jeremias Berg and Bart Boergarts and Matti Järvisalo: Certifying Pareto-Optimality in Multi-Objective Maximum Satisfiability, TACAS 2025.

Dependencies

~7–19MB
~305K SLoC