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The structured program theorem, also called the Böhm–Jacopini theorem, [1] [2] is a result in programming language theory. It states that a class of control-flow graphs (historically called flowcharts in this context) can compute any computable function if it combines subprograms in only three specific ways ( control structures ).
The structured program theorem proved that the goto statement is not necessary to write programs that can be expressed as flow charts; some combination of the three programming constructs of sequence, selection/choice, and repetition/iteration are sufficient for any computation that can be performed by a Turing machine, with the caveat that ...
Structured programming is a programming paradigm aimed at improving the clarity, quality, and development time of a computer program by making specific disciplined use of the structured control flow constructs of selection (if/then/else) and repetition (while and for), block structures, and subroutines.
Structure theorem for finitely generated modules over a principal ideal domain (abstract algebra) Structure theorem for Gaussian measures (measure theory) Structured program theorem (computer science) Sturm's theorem (theory of equations) Sturm–Picone comparison theorem (differential equations) Subspace theorem (Diophantine approximation)
The NIST Dictionary of Algorithms and Data Structures [1] is a reference work maintained by the U.S. National Institute of Standards and Technology.It defines a large number of terms relating to algorithms and data structures.
Corrado Böhm (17 January 1923 – 23 October 2017) was an Italian computer scientist and Professor Emeritus at the University of Rome "La Sapienza", known especially for his contributions to the theory of structured programming, constructive mathematics, combinatory logic, lambda calculus, and the semantics and implementation of functional programming languages.
A declarative programming program describes what the problem is, not how to solve it. The program is structured as a set of properties to find in the expected result, not as a procedure to follow. Given a database or a set of rules, the computer tries to find a solution matching all the desired properties.
The advantage is that the computational complexity is lower, and longer tasks can be planned by the solver. Identifying new macro operators for a domain can be realized with genetic programming. [4] The idea is, not to plan the domain itself, but in the pre-step, a heuristics is created that allows the domain to be solved much faster.