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

Linear Programming (LP) is a mathematical method used to find the best outcome in a model whose requirements are represented by linear relationships. It's like playing a game where you need to achieve the highest score (maximize) or the lowest score (minimize) under certain rules. These rules are your constraints, like how much money you can spend or how many hours you have. The score you're trying to optimize is called the objective function, and it's also a linear equation. LP helps you figure out the best way to play this game, balancing all the rules, to achieve your goal, whether it's making the most profit, using the least resources, or something similar. It's a powerful tool for decision-making in business, engineering, economics, and more.

make a decision D

when You solve a problem P 

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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.