# Function problem

In computational complexity theory, a **function problem** is a computational problem where a single output (of a total function) is expected for every input, but the output is more complex than that of a decision problem, that is, it isn't just YES or NO.

## Contents

## Formal definition

A functional problem is defined as a relation over a cartesian product over strings of an arbitrary alphabet :

An algorithm solves if for every input the algorithm either produces an output such that or decides that no such exists.

## Examples

A well-known function problem is given by the Functional Boolean Satisfyability Problem, **FSAT** for short. The problem, which is closely related to the **SAT** decision problem can be formulated as follows:

Given a boolean formula with variables , find an assignment such that evaluates to or decide that no such assignment exists.

In this case the relation is given by tuples of suitably encoded boolean formulas and satisfying assignments.

Other notable examples include the travelling salesman problem, which asks for the route taken by the salesman, and the integer factorization problem, which asks for the list of factors.

## Relationship to other complexity classes

Consider an arbitrary decision problem in the class **NP**. By definition each problem instance which are answered 'yes' have a certificate which servers as a proof for the 'yes' answer. Thus, the set of these tuples forms a relation. The complexity class derived from this transformation is denoted by or **FNP** for short. The mapping of the complexity class **P **is denoted by **FP**. The class **FP** is the set of function problems which can be solved by a deterministic Turing machine in polynomial time, whereas **FNP** is the set of function problems which can be solved by a non-deterministic Turing machine in polynomial time.

## Self-reducibility

Observe that the problem **FSAT **introduced above can be solved using only polynomially many calls to a subroutine which decides the **SAT** problem: An algorithm can first ask whether the formula is satisfiable. After that the algorithm can fix variable to TRUE and ask again. If the resulting formula is still satisfiable the algorithm keeps fixed to TRUE and continues to fix , otherwise it decides that has to be FALSE and continues. Thus, **FSAT **is solvable in polynomial time using an oracle deciding **SAT**. In general, a problem in **NP** is called self-reducible if its function variant can be solved in polynomial time using an oracle deciding the original problem. Every **NP-complete **problem is self-reducible. It is conjectured that the integer factorization problem is not self-reducible.

## Reductions and complete problems

Function problems can be reduced much like decision problems: Given function problems and we say that reduces to if there exists polynomially-time computable functions and such that for all instances and it holds that

It is therefore possible to define** FNP-complete **problems analogous to the NP-complete problem:

A problem is **FNP-complete **if every problem in **FNP **can be reduced to . The complexity class of **FNP-complete** problems is denoted by **FNP-C** or **FNPC**. It coincides with . Hence the problem** FSAT **is also an **FNP-complete** problem, and it holds that if and only if .

## Total function problems

The relation used to define function problems has the drawback of being incomplete: Not every input has a counterpart such that . Therefore the question of computability of proofs is not separated from the question of their existence. To overcome this problem it is convenient to consider the restriction of function problems to total relations yielding the class** TFNP **as a subclass of **FNP**. This class contains problems such as the computation of pure Nash equilibria in certain strategic games where a solution is guaranteed to exist. It has been shown that . In addition, if **TFNP **contains any** FNP-complete **problem it follows that .

## References

- Raymond Greenlaw, H. James Hoover,
*Fundamentals of the theory of computation: principles and practice*, Morgan Kaufmann, 1998, ISBN 1-55860-474-X, p. 45-51 - Elaine Rich,
*Automata, computability and complexity: theory and applications*, Prentice Hall, 2008, ISBN 0-13-228806-0, section 28.10 "The problem classes FP and FNP", pp. 689-694