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Linear inequality. In mathematics a linear inequality is an inequality which involves a linear function. A linear inequality contains one of the symbols of inequality: [ 1] < less than. > greater than. ≤ less than or equal to. ≥ greater than or equal to. ≠ not equal to.
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 .
For instance, to solve the inequality 4x < 2x + 1 ≤ 3x + 2, it is not possible to isolate x in any one part of the inequality through addition or subtraction. Instead, the inequalities must be solved independently, yielding x < 1 / 2 and x ≥ −1 respectively, which can be combined into the final solution −1 ≤ x < 1 / 2 .
Income inequality has fluctuated considerably in the United States since measurements began around 1915, moving in an arc between peaks in the 1920s and 2000s, with a 30-year period of relatively lower inequality between 1950 and 1980. The U.S. has the highest level of income inequality among its (post-)industrialized peers. [ 1]
Inequality has risen in most developed countries since the 1960s, so graphs of inequality over time no longer display a Kuznets curve. Piketty has argued that the decline in inequality over the first half of the 20th century was a once-off effect due to the destruction of large concentrations of wealth by war and economic depression.
Cauchy–Schwarz inequality (Modified Schwarz inequality for 2-positive maps [27]) — For a 2-positive map between C*-algebras, for all , in its domain, () ‖ ‖ (), ‖ ‖ ‖ ‖ ‖ ‖. Another generalization is a refinement obtained by interpolating between both sides of the Cauchy–Schwarz inequality:
Chebyshev's inequality. In probability theory, Chebyshev's inequality (also called the Bienaymé–Chebyshev inequality) provides an upper bound on the probability of deviation of a random variable (with finite variance) from its mean. More specifically, the probability that a random variable deviates from its mean by more than is at most ...
Bernoulli's inequality. An illustration of Bernoulli's inequality, with the graphs of and shown in red and blue respectively. Here, In mathematics, Bernoulli's inequality (named after Jacob Bernoulli) is an inequality that approximates exponentiations of . It is often employed in real analysis. It has several useful variants: [ 1]