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Cauchy–Schwarz inequality. The Cauchy–Schwarz inequality (also called Cauchy–Bunyakovsky–Schwarz inequality) [1][2][3][4] is an upper bound on the inner product between two vectors in an inner product space in terms of the product of the vector norms. It is considered one of the most important and widely used inequalities in mathematics.
Lagrange's identity for complex numbers has been obtained from a straightforward product identity. A derivation for the reals is obviously even more succinct. Since the Cauchy–Schwarz inequality is a particular case of Lagrange's identity, [4] this proof is yet another way to obtain the CS inequality. Higher order terms in the series produce ...
The mean value theorem is a generalization of Rolle's theorem, [citation needed] which assumes , so that the right-hand side above is zero. The mean value theorem is still valid in a slightly more general setting. One only needs to assume that is continuous on , and that for every in the limit.
Viktor Bunyakovsky. Baron Augustin-Louis Cauchy FRS FRSE (UK: / ˈkoʊʃi / KOH-shee, / ˈkaʊʃi / KOW-shee, [1][2] US: / koʊˈʃiː / koh-SHEE; [2][3] French: [oɡystɛ̃ lwi koʃi]; 21 August 1789 – 23 May 1857) was a French mathematician, engineer, and physicist. He was one of the first to rigorously state and prove the key theorems of ...
In mathematics, the QM-AM-GM-HM inequalities, also known as the mean inequality chain, state the relationship between the harmonic mean, geometric mean, arithmetic mean, and quadratic mean (also known as root mean square). Suppose that are positive real numbers. Then. These inequalities often appear in mathematical competitions and have ...
The Cauchy–Schwarz inequality implies the inner product is jointly continuous in norm and can therefore be extended to the completion. The action of A {\displaystyle A} on E {\displaystyle E} is continuous: for all x {\displaystyle x} in E {\displaystyle E}
The Cauchy–Schwarz inequality is met with equality when the two vectors involved are collinear. In the way it is used in the above proof, this occurs when all the non-zero eigenvalues of the Gram matrix G {\displaystyle G} are equal, which happens precisely when the vectors { x 1 , … , x m } {\displaystyle \{x_{1},\ldots ,x_{m ...
The triangle inequality is a defining property of norms and measures of distance. This property must be established as a theorem for any function proposed for such purposes for each particular space: for example, spaces such as the real numbers, Euclidean spaces, the L p spaces (p ≥ 1), and inner product spaces.