What is Structural complexity theory?

In computational complexity theory, L is the complexity class containing decision problems that can be solved by a deterministic Turing machine using a logarithmic amount of writable memory space. Formally,...

In computational complexity theory, the average-case complexity of an algorithm is the amount of some computational resource used by the algorithm, averaged over all possible inputs. It is frequently contrasted...

Programming complexity is a term that includes many properties of a piece of software, all of which affect internal interactions. According to several commentators, there is a distinction between the...

In computational complexity theory, asymptotic computational complexity is the usage of asymptotic analysis for the estimation of computational complexity of algorithms and computational problems, commonly associated with the usage of...

Randomized Logarithmic-space , sometimes called RLP , is the complexity class of computational complexity theory problems solvable in logarithmic space and polynomial time with probabilistic Turing machines with one-sided error....

In computational complexity theory, BPL , sometimes called BPLP , is the complexity class of problems solvable in logarithmic space and polynomial time with probabilistic Turing machines with two-sided error....

Many first principles in quantum field theory are explained, or get further insight, in string theory.

In computational complexity theory, NP is a complexity class used to classify decision problems. NP is the set of decision problems for which the problem instances, where the answer is...

Forecasting complexity is a measure of complexity put forward by the physicist Peter Grassberger. It was later renamed "statistical complexity" by James P. Crutchfield and Karl Young.

The law of conservation of complexity, also known as Tesler's Law, or Waterbed Theory, is an adage in human–computer interaction stating that every application has an inherent amount of complexity...