The book describes T(n) as a function that represents the number of assignment statements in the algorithm (note: this is a very simplified interpretation, but works for the example the book is using). The book then describes the order of magnitude function (O(n)).Briefly (in one sentence) state the relationship between O(n) and T(n)

What does O(n) represent in Big O Notation?*0/1a. Constant time complexityb. Linear time complexityQuadratic time complexity

t is a function that counts the number of character comparisons during the search.           It is a function that calculates the length of the longest proper prefix of the pattern that is also a suffix.           It is a function that calculates the Levenshtein distance between the text and pattern.           It is a function used to measure the amount of memory consumed by the algorithm.

The best-case time complexity of an algorithm is O(1). What does this mean?

What does the Big O notation primarily describe?

What does the term 'Big O' represent in the context of time complexity analysis?