# Complexity Theory

Over the course of this semester, we have considered many different problems, data structures and algorithms. Aside from knowing what good solutions are to common problems, it is also useful to understand how computer algorithms are classified according to its complexity. This section of the notes looks at complexity theory.

## Undecidable Problems

Some problems like the halting problems are undecidable. The halting problem is described simply as this... is it possible to write a program that will determine if any program has an infinite loop.

The answer to this question is as follows... suppose that we can write such a program. The program InfiniteCheck will do the following. It will accept as input a program. If the program it accepts gets stuck in an infinite loop it will print "program stuck" and terminate. If it the program does terminate, the InfiniteCheck program will go into an infinite loop.

Now, what if we give the InfiniteCheck the program InfiniteCheck as the input for itself.

If this is the case, then if InfiniteCheck has an infinite loop, it will terminate.

If infiniteCheck terminates, it will be stuck in an infinite loop because it terminated.

Both these statements are contradictory. and thus, such a program cannot exist.

## P class Problems

Any problem that can be solved in polynomial time is considered to be class P. For example, sorting is a class P problem because we have several algorithms who's solution is in polynomial time.

## NP class Problems

When we talk about "hard" problems then, we aren't talking about the impossible ones like the halting problem. We are instead talking about problems where its possible to find a solution, just that no good solution is currently known.

The NP, in NP class stands for non-deterministic polynomial time. Our computers today are deterministic machines. That is, instructions are executed one after the other. You can map out for a given input what the execution pathway of the program will be. A non-deterministic machine is a machine that has a choice of what action to take after each instruction and furthermore, should one of the actions lead to a solution, it will always choose that action.

A problem is in the NP class if we can **verify** that a given positive solution to our problem is correct in polynomial time. In other words, you don't have to find the solution in polynomial time, just verify that a solution is correct in polynomial time.

Note that all problems of class P are also class NP.

## NP-Complete Problems

A subset of the NP class problems is the NP-complete problems. NP-complete problems are problems where any problem in NP can be polynomially reduced to it. That is, a problem is considered to be NP-complete if it is able to provide a mapping from any NP class problem to it and back.

## NP-Hard

A problem is NP-Hard if any problem NP problem can be mapped to it and back in polynomial time. However, the problem does not need to be NP... that is a solution does not need to be verified in polynomial time