Types
Decision problem
A decision problem is a computational problem where the answer for every instance is either yes or no. An example of a decision problem is primality testing:
- "Given a positive integer n, determine if n is prime."
A decision problem is typically represented as the set of all instances for which the answer is yes. For example, primality testing can be represented as the infinite set
- L = {2, 3, 5, 7, 11, ...}
Search problem
In a search problem, the answers can be arbitrary strings. For example, factoring is a search problem where the instances are (string representations of) positive integers and the solutions are (string representations of) collections of primes.
A search problem is represented as a relation consisting of all the instance-solution pairs, called a search relation. For example, factoring can be represented as the relation
- R = {(4, 2), (6, 2), (6, 3), (8, 2), (9, 3), (10, 2), (10, 5)...}
which consist of all pairs of numbers (n, p), where p is a prime factor of n.
Counting problem
A counting problem asks for the number of solutions to a given search problem. For example, a counting problem associated with factoring is
- "Given a positive integer n, count the number of nontrivial prime factors of n."
A counting problem can be represented by a function f from {0, 1}* to the nonnegative integers. For a search relation R, the counting problem associated to R is the function
- fR(x) = |{y: R(x, y) }|.
Optimization problem
An optimization problem asks for finding a "best possible" solution among the set of all possible solutions to a search problem. One example is the maximum independent set problem:
- "Given a graph G, find an independent set of G of maximum size."
Optimization problems are represented by their objective function and their constraints.
Function problem
In a function problem 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". One of the most famous examples is the traveling salesman problem:
- "Given a list of cities and the distances between each pair of cities, find the shortest possible route that visits each city exactly once and returns to the origin city."
It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science.
Promise problem
Main article: Promise problem
In computational complexity theory, it is usually implicitly assumed that any string in {0, 1}* represents an instance of the computational problem in question. However, sometimes not all strings {0, 1}* represent valid instances, and one specifies a proper subset of {0, 1}* as the set of "valid instances". Computational problems of this type are called promise problems.
The following is an example of a (decision) promise problem:
- "Given a graph G, determine if every independent set in G has size at most 5, or G has an independent set of size at least 10."
Here, the valid instances are those graphs whose maximum independent set size is either at most 5 or at least 10.
Decision promise problems are usually represented as pairs of disjoint subsets (Lyes, Lno) of {0, 1}*. The valid instances are those in Lyes ∪ Lno. Lyes and Lno represent the instances whose answer is yes and no, respectively.
Promise problems play an important role in several areas of computational complexity, including hardness of approximation, property testing, and interactive proof systems.
