Scuttle is a multi-objective MaxSAT solver written in Rust and based on the RustSAT library and the CaDiCaL SAT solver.
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 incpaior24/. - TACAS'25 (
tacas25): "Certifying Pareto-Optimality in Multi-Objective Maximum Satisfiability" [7]. Additional material intacas25/.
| 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] |
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.
sol-tightening: includes heuristic tightening of solutions after they are found in the buildmaxpre: includes preprocessing with MaxPre in the build
Apparently "scuttle" is one of multiple term for a group of crabs, which seemed fitting for a multi-objective solver in Rust.
- [1] Takehide Soh and Mutsunori Banbara and Naoyuki Tamura and Daniel Le Berre: Solving Multiobjective Discrete Optimization Problems with Propositional Minimal Model Generation, CP 2017.
- [2] Miyuki Koshimura and Hidetomo Nabeshima and Hiroshi Fujita and Ryuzo Hasegawa: Minimal Model Generation with Respect to an Atom Set, FTP 2009.
- [3] Joao Cortes and Ines Lynce and Vasco M. Maquinho: New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization, TACAS 2023.
- [4] Christoph Jabs and Jeremias Berg and Andreas Niskanen and Matti Järvisalo: MaxSAT-Based Bi-Objective Boolean Optimization, SAT 2022.
- [5] Christoph Jabs and Jeremias Berg and Hannes Ihalainen and Matti Järvisalo: Preprocessing in SAT-Based Multi-Objective Combinatorial Optimization, CP 2023.
- [6] Christoph Jabs and Jeremias Berg and Matti Järvisalo: Core Boosting in SAT-Based Multi-Objective Optimization, CPAIOR 2024.
- [7] Christoph Jabs and Jeremias Berg and Bart Boergarts and Matti Järvisalo: Certifying Pareto-Optimality in Multi-Objective Maximum Satisfiability, TACAS 2025.