An algorithm is said to have a linear time complexity when the running time increases linearly with the length of the input. When the function involves checking all the values in input data, with this order O(n). The above code shows that based on the length of the array (n), the run time will get linearly increased.
In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm Table of common time Constant timeLinear timeQuasilinear time
Unproven computational hardness assumption
In computational complexity theory, the exponential time hypothesis is an unproven computational hardness assumption that was formulated by Impagliazzo & Paturi (1999).
It states that satisfiability of 3-CNF Boolean formulas cannot be solved in subexponential time, mwe-math-element>.
More precisely, the usual form of the hypothesis asserts the existence of a number mwe-math-element> such that all algorithms that correctly solve this problem require time at least mwe-math-element>.
The exponential time hypothesis, if true, would imply that P ≠ NP, but it is a stronger statement.
It implies that many computational problems are equivalent in complexity, in the sense that if one of them has a subexponential time algorithm then they all do, and that many known algorithms for these problems have optimal or near-optimal time nowrap>complexity.
