-
XCSP3-core: A Format for Representing Constraint Satisfaction/Optimization Problems
In this document, we introduce XCSP3-core, a subset of XCSP3 that allows...
read it
-
Solution Dominance over Constraint Satisfaction Problems
Constraint Satisfaction Problems (CSPs) typically have many solutions th...
read it
-
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games
Decision-making problems can be modeled as combinatorial optimization pr...
read it
-
Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem
The job-shop scheduling is one of the most studied optimization problems...
read it
-
Model-Driven Constraint Programming
Constraint programming can definitely be seen as a model-driven paradigm...
read it
-
Short Portfolio Training for CSP Solving
Many different approaches for solving Constraint Satisfaction Problems (...
read it
-
Exploiting Single-Cycle Symmetries in Continuous Constraint Problems
Symmetries in discrete constraint satisfaction problems have been explor...
read it
PYCSP3: Modeling Combinatorial Constrained Problems in Python
In this document, we introduce PYCSP3, a Python library that allows us to write models of combinatorial constrained problems in a simple and declarative way. Currently, with PyCSP3, you can write models of constraint satisfaction and optimization problems. More specifically, you can build CSP (Constraint Satisfaction Problem) and COP (Constraint Optimization Problem) models. Importantly, there is a complete separation between modeling and solving phases: you write a model, you compile it (while providing some data) in order to generate an XCSP3 instance (file), and you solve that problem instance by means of a constraint solver. In this document, you will find all that you need to know about PYCSP3, with more than 40 illustrative models.
READ FULL TEXT
Comments
There are no comments yet.