make a decision D
when You solve a problem P
???
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.
Vector of Parameters: This is a set of variables or conditions that characterize the system or scenario. They can be input conditions, system states, or any other relevant metrics that describe the system.
Objective Function: This is a mathematical function that quantifies how far the current system's performance is from the desired performance. The goal is usually to minimize or maximize this function.
Constraints: These are the bounds or limitations within which the system operates. In engineering problems, constraints can arise from physical limitations, safety requirements, budgetary restrictions, etc. The solution must satisfy these constraints.