Asymptotic complexity classes remain identical between models for polynomial-time problems; differences matter only for constant factors, which formal analysis shouldn't depend on.

?Rate how convincing each reason is below to see the overall strength.

No one has weighed in yet. Be the first to share reasons for or against this statement.

Asymptotic complexity(in computer science and mathematics): A way of measuring how much harder a computer problem gets as the amount of data grows—focusing on the worst-case scenario as the data becomes very large.
Complexity classes(as used in computer science and philosophy of computation): In computer science, groups of problems sorted by how hard they are to solve—roughly, how much computing power and time they require.
Constant factors(used in computer science and mathematics): The fixed numbers or multipliers in a formula that don't change based on the problem size—for example, if one method is always 2 times slower than another, that '2' is the constant factor.
Formal analysis(as the method Kim used): A careful, step-by-step logical breakdown of an idea using precise definitions and reasoning, kind of like diagramming a sentence but for abstract concepts.

Based on where you are in your exploration