MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The lower bound for an algorithm (or a problem, as explained later) is denoted by the symbol , pronounced “big-Omega” or just “Omega”. The following definition for is symmetric with the definition of big-Oh.

Detailed explanation-2: -Similar to big O notation, big Omega() function is used in computer science to describe the performance or complexity of an algorithm. If a running time is (f(n)), then for large enough n, the running time is at least k⋅f(n) for some constant k.

Detailed explanation-3: -Big-Oh notation describes an upper bound. In other words, big-Oh notation states a claim about the greatest amount of some resource (usually time) that is required by an algorithm for some class of inputs of size n (typically the worst such input, the average of all possible inputs, or the best such input).

Detailed explanation-4: -Lower bound of an algorithm is shown by the asymptotic notation called Big Omega (or just Omega).

Detailed explanation-5: -Big- is used as a tight upper bound on the growth of an algorithm’s effort (this effort is described by the function f(n)), even though, as written, it can also be a loose upper bound. “Little-” (()) notation is used to describe an upper bound that cannot be tight.