A Practical Introduction to Constraint Programming Using CP-SAT and Python
Constraint programming (CP) is a declarative paradigm for solving discrete optimization problems using variables and constraints. CP-SAT, an open-source solver by Google, can handle complex problems efficiently, like fair contribution distribution and employee scheduling.
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Constraint programming (CP) is a declarative paradigm used to solve discrete optimization problems by describing the desired result through variables and constraints. Variables represent what is sought, with associated domains, while constraints define relationships between variables. Solutions are assignments of values to variables satisfying constraints. CP can handle complex concepts with global constraints, like ensuring variables have different values. A solver interprets the model and returns a valid solution. Adding objectives allows maximizing or minimizing expressions without compromising constraints. An example illustrates distributing contributions fairly among individuals. CP-SAT, an open-source solver by Google, can be used with Python to solve real-world problems. A practical example involves scheduling employees for shifts in a store, ensuring coverage, role assignments, and shift constraints. By defining variables, constraints, and objectives, CP can efficiently solve intricate optimization challenges.
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8 commentsIt is a shame, as most programs work against the ideas here by trying to have a singular representation of their data. This is just not reasonable for most things and leads to a lot of contortions to get the algorithms to work on a new representation.
This article touches on it with the brief touch of declarative at the top. I always regret that more of my code is not translating between representations more often. You can wind up with very concise representations when you do this, and then you can get a double bonus by having things run faster by virtue of being concise.
(And, yes, I realize I'm basically describing many data pipelines. Where you spend most of your time translating and fanning out data to places for more compute to be done on it.)
MiniZinc is a constraint programming system. There is a good Coursera class using MiniZinc.
I love the subject and reading this brought back a lot of memories. Also the realization that translating constraints to a model (variables, structure etc) is 90% of the work and the most difficult part.
It so often bothers me that I have to guesstimate some values for parameters I don't initially care about, instead of constraining the parameters I care about and then optimizing the rest.
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