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In computer science, recursion is a method of solving a computational problem where the solution depends on solutions to smaller instances of the same problem. [ 1 ] [ 2 ] Recursion solves such recursive problems by using functions that call themselves from within their own code.
Roberts (p. 171) gives a related example in Java, using a Class to represent a stack frame. The example given is a solution to the Tower of Hanoi problem wherein a stack simulates polymorphic recursion with a beginning, temporary and ending nested stack substitution structure. [5]
A recursive step — a set of rules that reduces all successive cases toward the base case. For example, the following is a recursive definition of a person's ancestor. One's ancestor is either: One's parent (base case), or; One's parent's ancestor (recursive step). The Fibonacci sequence is another classic example of recursion: Fib(0) = 0 as ...
Anonymous recursion is primarily of use in allowing recursion for anonymous functions, particularly when they form closures or are used as callbacks, to avoid having to bind the name of the function. Anonymous recursion primarily consists of calling "the current function", which results in direct recursion .
Mutual recursion is very common in functional programming, and is often used for programs written in LISP, Scheme, ML, and similar programming languages. For example, Abelson and Sussman describe how a meta-circular evaluator can be used to implement LISP with an eval-apply cycle. [7] In languages such as Prolog, mutual recursion is almost ...
Another example is a similar singly linked type in Java: class List < E > { E value ; List < E > next ; } This indicates that non-empty list of type E contains a data member of type E, and a reference to another List object for the rest of the list (or a null reference to indicate that this is the end of the list).
If the matrix A has no columns, the current partial solution is a valid solution; terminate successfully. Otherwise choose a column c (deterministically). Choose a row r such that A r, c = 1 (nondeterministically). Include row r in the partial solution. For each column j such that A r, j = 1, for each row i such that A i, j = 1, delete row i ...
For example, this approach is used in some efficient FFT implementations, where the base cases are unrolled implementations of divide-and-conquer FFT algorithms for a set of fixed sizes. [11] Source-code generation methods may be used to produce the large number of separate base cases desirable to implement this strategy efficiently. [11]