Algorithm analysis

See: theta notation, Big-O notation, omega notation

Algorithm analysis generally refers to the "complexity analysis" of an algorithm: how the characteristics of the algorithm changes with respect to changes in parameters.

In other words, how does the input size of an algorithm affect: 1 - runtime (instructional steps) - space (in memory) - N / W (data transferred / network consumption) - power consumption (for battery life) - CPU registers (physical space used on the processor)

Algorithm analysis is useful to measure the efficiency of an algorithm, since it is machine-independent.

  1. https://www.youtube.com/watch?v=xGYsEqe9Vl0 ↩