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Computational complexity theory studies the resources required to solve families of computational problems. A statement specifies an input encoding and a model of computation, then measures resources such as running time, computer memory or query complexity as the encoded input length grows. An algorithm's upper bound describes one method; a problem lower bound constrains every permitted algorithm in the stated model.

For decision problems, a complexity class collects problems with a shared resource guarantee. P uses deterministic polynomial time; NP permits polynomially bounded witnesses checked in polynomial time. The distinction separates finding an answer from verifying a supplied certificate.^[ref]

The cost model is part of the mathematics. Bit complexity charges for arithmetic on operands of growing size, while a comparison model counts comparisons of keys. A polynomial-time algorithm also need not be practical at every scale: its exponent and constants still affect actual running time. These distinctions make complexity bounds useful for comparing both mathematical possibilities and concrete implementations.

A computer is a physical system that represents information and carries out defined operations on it. In a stored-program design, instructions and data are held in computer memory, and a processor interprets instructions to update state. A simple program can read two represented numbers, add them and store the result; the represented bit patterns acquire meaning through the chosen encoding. Digital computers use discrete states, while analogue computers represent quantities through continuously varying physical variables. A mathematical model of computation abstracts these mechanisms to study capability and resource use.

A model of computation specifies how inputs are represented, which states and operations are available, how execution proceeds and what counts as its output and resource cost. Examples include Turing machines, random-access machines, Boolean circuits and the lambda calculus. A mathematical algorithmic claim is interpreted within such a model, even when the model is left implicit.

For example, adding two integers is one instruction in a unit-cost machine model. In a bit-cost model, operating on arbitrarily long integers requires time depending on their encoded lengths. A bound counting arithmetic operations therefore need not be a bound on bit complexity. Likewise, a circuit family can supply separate hardware for each input length, while a uniformity requirement asks one algorithm to generate those circuits. Stating the model makes efficiency comparisons meaningful and separates questions of computability from the cost of a chosen representation.

A Turing machine is a model of computation with finite control, a tape of cells and a head scanning one cell at a time. An instruction of a Turing machine uses the current state and scanned symbol to determine a new state, a written symbol and a head movement. The finite transition rules form the machine's description; the tape supplies storage without a fixed global bound.

In a standard input convention, a finite word occupies part of an otherwise blank tape. A Turing machine configuration records the state, head position and tape contents, so the next-configuration relation determines each computation step. A machine can accept, reject or continue indefinitely. Recognizing a formal language permits nontermination on some nonmembers; deciding it requires an answer on every input.

The separation between a finite rule table and unbounded reusable storage makes the model useful for proving general statements about algorithms. Alan Turing's 1936 paper introduced this style of machine to analyze effective calculation; its original examples produce successive digits of numbers.^[ref] Multitape Turing machines change the storage organization and possible step counts while retaining the same computability power.