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Idempotence (UK: / ˌ ɪ d ɛ m ˈ p oʊ t ən s /, [1] US: / ˈ aɪ d ə m-/) [2] is the property of certain operations in mathematics and computer science whereby they can be applied multiple times without changing the result beyond the initial application.
An idempotent matrix is always diagonalizable. [3] Its eigenvalues are either 0 or 1: if is a non-zero eigenvector of some idempotent matrix and its associated eigenvalue, then = = = = =, which implies {,}.
R is a transitive relation, meaning that R ∘ R ⊆ R. Equivalently, in terms of individual elements, for every x, y, and z for which xRy and yRz are both true, xRz is also true. R ∘ R ⊇ R. Equivalently, in terms of individual elements, for every x and z for which xRz is true, there exists y with xRy and yRz both true.
One may define a partial order on the idempotents of a ring as follows: if a and b are idempotents, we write a ≤ b if and only if ab = ba = a. With respect to this order, 0 is the smallest and 1 the largest idempotent. For orthogonal idempotents a and b, a + b is also idempotent, and we have a ≤ a + b and b ≤ a + b.
In computer science, an operation, function or expression is said to have a side effect if it has any observable effect other than its primary effect of reading the value of its arguments and returning a value to the invoker of the operation.
In this case, idempotent means that projecting twice is the same as projecting once. The restriction to a subspace of a projection is also called a projection, even if the idempotence property is lost. An everyday example of a projection is the casting of shadows onto a plane (sheet of paper): the projection of a point is its shadow on the ...
Over 130 years after his gruesome murders in East London, England, the descendants of his victims are looking to unmask the identity of the serial killer popularly known as Jack the Ripper. The ...
It is emphasized that the definition of depends on context. For instance, had L {\displaystyle L} been declared as a subset of Y , {\displaystyle Y,} with the sets Y {\displaystyle Y} and X {\displaystyle X} not necessarily related to each other in any way, then L ∁ {\displaystyle L^{\complement }} would likely mean Y ∖ L {\displaystyle Y ...