solv

Solv is a lightweight, open-source solver library designed for constraint satisfaction and optimization problems. It provides a declarative modelling language and a backtracking search engine with constraint propagation to find feasible assignments for variables under given constraints.

Modeling and language bindings: The core library exposes an API in multiple languages, with primary bindings for Python and JavaScript and community ports to Rust and Go. It uses finite-domain variables, supports integer and boolean types, and includes global constraints such as all-different and sum. The modelling language allows users to declare variables, specify domains, and express constraints in a readable form.

Solving features: Solv supports optimization tasks, including minimize and maximize objectives, with options for lexicographic or Pareto approaches. It employs constraint propagation and backtracking, augmented by heuristics such as minimum remaining values and degree-based ordering. The system can integrate with external solvers for SAT or SMT through plug-ins, enabling hybrid solving workflows.

Applications: Typical use cases include scheduling, timetabling, resource allocation, puzzle solving, and educational demonstrations of constraint programming. The library is designed to be easy to embed in other software and to run on modest hardware, making it suitable for teaching, prototyping, and lightweight production tasks.

History: The project originated in 2019 from a collective of researchers and developers seeking accessible constraint programming tools. Solv released its first stable version in 2020 under a permissive license, followed by successive updates that added language bindings, improved performance, and expanded constraint support.

Reception: Solv has a modest but active community. It is praised for simplicity and readability but may be outpaced on very large-scale problems by industrial-grade solvers, leading to a niche but steady adoption in education and small-to-medium applications. See also constraint programming, constraint satisfaction problems, SAT solvers, and optimization.