Middle English: from Old French constraindre, from Latin constringere ‘bind tightly together’.

make a decision D

when You solve a problem P 

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Constraint programming is a programming paradigm where relationships between variables are expressed as constraints. The objective is to find values for these variables that satisfy all given constraints. It is particularly useful for solving combinatorial problems, such as scheduling, planning, and resource allocation, where traditional algorithms might be inefficient. Instead of specifying steps to achieve a solution, one defines the desired properties of a solution, and the system determines a valid assignment, if one exists.

Focus: CP is more general and can handle a wide variety of constraints, not just linear ones. It can deal with logical conditions, like "either-or" situations, and can include non-linear relationships.

Objective: CP doesn't necessarily have an objective function to optimize. Instead, it focuses on finding solutions that satisfy all the given constraints.

Method: It uses different algorithms than LP, often based on search techniques, like backtracking or heuristics.

Constraints: Constraints in CP can be diverse - linear, non-linear, logical conditions, etc. For example, a constraint could be that a certain task must be done before another can start.

Solutions: Solutions in CP are often discrete (like whole numbers) and can involve deciding between different options or scenarios.