The term "pspace" is often used in computer science, specifically in complexity theory. It refers to the class of decision problems that can be solved by a deterministic Turing machine using polynomial space. The spelling of "pspace" is based on phonetics, with the initial "p" pronounced as a voiceless bilabial stop, the "s" as a voiceless alveolar fricative, and the ending "space" pronounced as it is spelled. The IPA phonetic transcription for "pspace" is /pi speɪs/.
PSPACE stands for Polynomial Space and it is a term used in computer science and computational complexity theory. It refers to a complexity class that defines the amount of memory required to solve a problem using a polynomial amount of computational steps, specifically using a polynomial amount of space.
In computational complexity theory, a complexity class defines the amount of resources, such as time or space, required to solve a particular problem. PSPACE is a class that consists of all decision problems that can be solved using a polynomial amount of memory space on a deterministic Turing machine.
More formally, PSPACE is the set of all languages that are decidable in polynomial space on a deterministic Turing machine. A deterministic Turing machine is a theoretical computing device that can simulate any other computational model and is widely used in the field of theoretical computer science.
Problems that fall under the PSPACE class have a computational complexity that is at most exponential in the input size, but not necessarily polynomial. This means that solving problems in PSPACE can be quite demanding and may require a significant amount of memory space compared to other complexity classes, such as P (Polynomial Time) or NP (Nondeterministic Polynomial Time).
Overall, PSPACE encompasses decision problems that can be solved using a polynomial amount of memory space and its study helps understand the limits and possibilities of computational power and efficiency.
The term "pspace" is derived from two other terms related to computer science: "P" and "space".
"P" is short for "polynomial". In computational complexity theory, problems that can be solved in polynomial time are categorized as P. These problems have algorithms with time complexities that can be bounded by a polynomial function. Essentially, P represents a set of problems that can be efficiently solved.
"Space" refers to the amount of memory or space required to solve a problem. In computational complexity theory, space complexity is the measure of the memory used by an algorithm to solve a problem.
Combining these two terms, "pspace" represents the complexity class "polynomial space". It refers to the set of problems that can be solved using a polynomial amount of memory or space. Psace includes problems that can be efficiently solved in terms of space utilization.