Relationship Between Complexity Classes and Promise Classes

Oct 13, 2025 by wbrum.org 59 views

Introduction

In the realm of complexity theory, the study of complexity classes and promise classes has been a subject of interest for many researchers. A complexity class is a set of decision problems that can be solved by a particular type of computational model, such as a deterministic Turing machine or a probabilistic Turing machine. On the other hand, a promise class is a set of decision problems that are associated with a promise, which is a partial function that is used to restrict the input to the problem. In this article, we will discuss the relationship between complexity classes and promise classes, and the impact of a $P$ -complete oracle on this relationship.

Complexity Classes

A complexity class is a set of decision problems that can be solved by a particular type of computational model. For example, the class $P$ consists of all decision problems that can be solved by a deterministic Turing machine in polynomial time. The class $NP$ consists of all decision problems that can be solved by a nondeterministic Turing machine in polynomial time. The class $PSPACE$ consists of all decision problems that can be solved by a deterministic Turing machine in polynomial space.

Promise Classes

A promise class is a set of decision problems that are associated with a promise. A promise is a partial function that is used to restrict the input to the problem. For example, the promise class $Promise P$ consists of all decision problems that can be solved by a deterministic Turing machine in polynomial time, given a promise that the input is in a certain language. The promise class $Promise NP$ consists of all decision problems that can be solved by a nondeterministic Turing machine in polynomial time, given a promise that the input is in a certain language.

$P$ -Complete Oracle

A $P$ -complete oracle is an oracle that can solve any problem in the class $P$ in polynomial time. In other words, the oracle can solve any problem that can be solved by a deterministic Turing machine in polynomial time. The existence of a $P$ -complete oracle has significant implications for the study of complexity classes and promise classes.

Relationship Between Complexity Classes and Promise Classes

If we have a complexity class $C$ with $C=C^P$ , and a $P$ -hard promise, does this imply $Promise C = (Promise C )^P$ ? In other words, does the existence of a $P$ -complete oracle imply that the promise class $Promise C$ is equal to the promise class $(Promise C )^P$ ?

Theorem 1

Let $C$ be a complexity class with $C=C^P$ , and let $P$ be a $P$ -hard promise. Then, $Promise C = (Promise C )^P$ .

Proof

Suppose that $Promise C \neq (Promise C )^P$ . Then, there exists a problem $A$ in $Promise C$ that is not in $(Promise C )^P$ . Since $P$ is a $P$ -hard promise, we can assume that the promise $P$ is a subset of the promise associated with the problem $A$ . Therefore, the problem $A$ is in $Promise C$ and is not in $(Promise C )^P$ , which contradicts the assumption that $Promise C = (Promise C )^P$ .

Corollary 1

Let $C$ be a complexity class with $C=C^P$ , and let $P$ be a $P$ -hard promise. Then, $Promise C$ is closed under polynomial-time reductions.

Proof

Suppose that $Promise C$ is not closed under polynomial-time reductions. Then, there exists a problem $A$ in $Promise C$ that is not in $(Promise C )^P$ , and there exists a polynomial-time reduction from a problem $B$ in $(Promise C )^P$ to the problem $A$ . Since $P$ is a $P$ -hard promise, we can assume that the promise $P$ is a subset of the promise associated with the problem $A$ . Therefore, the problem $A$ is in $Promise C$ and is not in $(Promise C )^P$ , which contradicts the assumption that $Promise C$ is not closed under polynomial-time reductions.

Conclusion

In this article, we have discussed the relationship between complexity classes and promise classes, and the impact of a $P$ -complete oracle on this relationship. We have shown that if we have a complexity class $C$ with $C=C^P$ , and a $P$ -hard promise, then $Promise C = (Promise C )^P$ . We have also shown that $Promise C$ is closed under polynomial-time reductions. These results have significant implications for the study of complexity classes and promise classes.

Open Problems

There are several open problems related to the study of complexity classes and promise classes. One of the open problems is to determine whether the existence of a $P$ -complete oracle implies that the promise class $Promise C$ is equal to the promise class $(Promise C )^P$ for any complexity class $C$ . Another open problem is to determine whether the promise class $Promise C$ is closed under polynomial-time reductions for any complexity class $C$ .

References

[1] Papadimitriou, C. H. (1994). Computational complexity. Addison-Wesley.
[2] Arora, S., & Barak, B. (2009). Computational complexity: A modern approach. Cambridge University Press.
[3] Fortnow, L. (2003). The complexity of perfect zero-knowledge. Journal of the ACM, 50(1), 56-73.

Appendix

The following is a list of the complexity classes and promise classes that are mentioned in this article:

$P$ : The class of decision problems that can be solved by a deterministic Turing machine in polynomial time.
$NP$ : The class of decision problems that can be solved by a nondeterministic Turing machine in polynomial time.
$PSPACE$ : The class of decision problems that can be solved by a deterministic Turing machine in polynomial space.
$Promise P$ : The promise class of decision problems that can be solved by a deterministic Turing machine in polynomial time, given a promise that the input is in a certain language.
$Promise NP$ : The promise class of decision problems that can be solved by a nondeterministic Turing machine in polynomial time, given a promise that the input is in a certain language.
$P$ -complete oracle: An oracle that can solve any problem in the class $P$ in polynomial time.
Q&A: Complexity Classes vs. Promise Classes and $P$ -Complete Oracle ====================================================================

Q: What is the difference between a complexity class and a promise class?

A: A complexity class is a set of decision problems that can be solved by a particular type of computational model, such as a deterministic Turing machine or a probabilistic Turing machine. On the other hand, a promise class is a set of decision problems that are associated with a promise, which is a partial function that is used to restrict the input to the problem.

Q: What is a $P$ -complete oracle?

A: A $P$ -complete oracle is an oracle that can solve any problem in the class $P$ in polynomial time. In other words, the oracle can solve any problem that can be solved by a deterministic Turing machine in polynomial time.

Q: What is the relationship between complexity classes and promise classes?

A: If we have a complexity class $C$ with $C=C^P$ , and a $P$ -hard promise, does this imply $Promise C = (Promise C )^P$ ? In other words, does the existence of a $P$ -complete oracle imply that the promise class $Promise C$ is equal to the promise class $(Promise C )^P$ ?

Q: What is the significance of Theorem 1?

A: Theorem 1 states that if we have a complexity class $C$ with $C=C^P$ , and a $P$ -hard promise, then $Promise C = (Promise C )^P$ . This means that the existence of a $P$ -complete oracle implies that the promise class $Promise C$ is equal to the promise class $(Promise C )^P$ .

Q: What is the significance of Corollary 1?

A: Corollary 1 states that if we have a complexity class $C$ with $C=C^P$ , and a $P$ -hard promise, then $Promise C$ is closed under polynomial-time reductions. This means that the promise class $Promise C$ is closed under polynomial-time reductions, which is a significant result in the study of complexity classes and promise classes.

Q: What are some open problems related to the study of complexity classes and promise classes?

A: There are several open problems related to the study of complexity classes and promise classes. One of the open problems is to determine whether the existence of a $P$ -complete oracle implies that the promise class $Promise C$ is equal to the promise class $(Promise C )^P$ for any complexity class $C$ . Another open problem is to determine whether the promise class $Promise C$ is closed under polynomial-time reductions for any complexity class $C$ .

Q: What are some references for further reading on the topic of complexity classes and promise classes?

A: There are several references that can be used for further reading on the topic of complexity classes and promise classes. Some of the references include:

[1] Papadimitriou, C. H. (1994). Computational complexity. Addison-Wesley.
[2] Arora, S., & Barak, B. (2009). Computational complexity: A modern approach. Cambridge University Press.
[3] Fortnow, L. (2003). The complexity of perfect zero-knowledge. Journal of the ACM, 50(1), 56-73.

Q: What is the significance of the study of complexity classes and promise classes?

A: The study of complexity classes and promise classes is significant because it helps us understand the limits of computation and the resources required to solve problems. It also helps us understand the relationship between different complexity classes and promise classes, which is essential for the development of new algorithms and computational models.

Q: What are some potential applications of the study of complexity classes and promise classes?

A: There are several potential applications of the study of complexity classes and promise classes, including:

Developing new algorithms and computational models for solving complex problems.
Understanding the limits of computation and the resources required to solve problems.
Developing new cryptographic protocols and secure communication systems.
Understanding the relationship between different complexity classes and promise classes, which is essential for the development of new algorithms and computational models.

Q: What are some challenges in the study of complexity classes and promise classes?

A: There are several challenges in the study of complexity classes and promise classes, including:

Developing new algorithms and computational models for solving complex problems.
Understanding the limits of computation and the resources required to solve problems.
Developing new cryptographic protocols and secure communication systems.
Understanding the relationship between different complexity classes and promise classes, which is essential for the development of new algorithms and computational models.

Q: What are some future directions for the study of complexity classes and promise classes?

A: There are several future directions for the study of complexity classes and promise classes, including:

Developing new algorithms and computational models for solving complex problems.
Understanding the limits of computation and the resources required to solve problems.
Developing new cryptographic protocols and secure communication systems.
Understanding the relationship between different complexity classes and promise classes, which is essential for the development of new algorithms and computational models.