Demand-driven computation is a programming paradigm that focuses on executing only the parts of a program that are necessary to produce the desired output, rather than executing the entire program from start to finish. In demand-driven computation, the execution of the program is driven by the demand for specific outputs or results, rather than a predetermined sequence of operations.
Demand-driven computation is often used in applications such as data processing, where only a subset of the data needs to be processed to produce the desired output. In these applications, demand-driven computation can be used to improve performance by reducing the amount of unnecessary computation that needs to be performed.
There are several techniques that can be used to implement demand-driven computation, including lazy evaluation, incremental computation, and memoization. Lazy evaluation involves delaying the evaluation of an expression until it is actually needed, while incremental computation involves only computing the parts of a program that have changed since the last time the output was computed. Memoization involves caching the results of previous computations and reusing them when possible.
Demand-driven computation can also be combined with other programming paradigms, such as functional programming or logic programming, to create more efficient and expressive programs. Overall, demand-driven computation is a powerful tool for optimizing computation and reducing unnecessary work in a wide range of applications.