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invariance
(redirected from Invariances)

   Also found in: Legal, Encyclopedia, Wikipedia 0.02 sec.
in·var·i·ant  (n-vâr-nt)
adj.
1. Not varying; constant.
2. Mathematics Unaffected by a designated operation, as a transformation of coordinates.
n.
An invariant quantity, function, configuration, or system.

in·vari·ance n.

invariance  (n-vâr-ns)
The property of remaining unchanged regardless of changes in the conditions of measurement. For example, the area of a surface remains unchanged if the surface is rotated in space; thus the area exhibits rotational invariance. In physics, invariance is related to conservation laws. For example, conservation of angular momentum is directly related to rotational invariance (the laws of physics don't depend on the angle of the reference point), conservation of energy is related to invariance over time (the laws of physics remain the same over time), and conservation of momentum is related to invariance over translations through space (the laws of physics don't depend on the position of the reference point). A form of invariance called Lorenz invariance is fundamental to the theory of Special Relativity. Invariance is also called symmetry. See also Noether's theorem.
ThesaurusLegend:  Synonyms Related Words Antonyms
Noun1.invariance - the quality of being resistant to variation
unchangeability, unchangeableness, unchangingness, changelessness - the quality of being unchangeable; having a marked tendency to remain unchanged
unvariedness - characterized by an absence of variation
2.invariance - the nature of a quantity or property or function that remains unchanged when a given transformation is applied to it; "the invariance of the configuration under translation"
constancy, stability - the quality of being enduring and free from change or variation; "early mariners relied on the constancy of the trade winds"
math, mathematics, maths - a science (or group of related sciences) dealing with the logic of quantity and shape and arrangement


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Theoretical generalizations are derived from invariances that lead us to find the driving force for common outcomes or behaviors.
These kernels are particularly useful where the input data may possess invariances or noise components that can interfere with the ability to accurately extract the desired information.
 
 
 
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