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  2. List of trigonometric identities - Wikipedia

    en.wikipedia.org/wiki/List_of_trigonometric...

    In trigonometry, trigonometric identities are equalities that involve trigonometric functions and are true for every value of the occurring variables for which both sides of the equality are defined.

  3. List of inequalities - Wikipedia

    en.wikipedia.org/wiki/List_of_inequalities

    Bennett's inequality, an upper bound on the probability that the sum of independent random variables deviates from its expected value by more than any specified amount Bhatia–Davis inequality , an upper bound on the variance of any bounded probability distribution

  4. QM-AM-GM-HM inequalities - Wikipedia

    en.wikipedia.org/wiki/QM-AM-GM-HM_Inequalities

    There are three inequalities between means to prove. There are various methods to prove the inequalities, including mathematical induction, the Cauchy–Schwarz inequality, Lagrange multipliers, and Jensen's inequality. For several proofs that GM ≤ AM, see Inequality of arithmetic and geometric means.

  5. Rearrangement inequality - Wikipedia

    en.wikipedia.org/wiki/Rearrangement_inequality

    The rearrangement inequality can be regarded as intuitive in the following way. Imagine there is a heap of $10 bills, a heap of $20 bills and one more heap of $100 bills.

  6. Young's inequality for products - Wikipedia

    en.wikipedia.org/wiki/Young's_inequality_for...

    Proof [2]. Since + =, =. A graph = on the -plane is thus also a graph =. From sketching a visual representation of the integrals of the area between this curve and the axes, and the area in the rectangle bounded by the lines =, =, =, =, and the fact that is always increasing for increasing and vice versa, we can see that upper bounds the area of the rectangle below the curve (with equality ...

  7. Khintchine inequality - Wikipedia

    en.wikipedia.org/wiki/Khintchine_inequality

    The uses of this inequality are not limited to applications in probability theory.One example of its use in analysis is the following: if we let be a linear operator between two L p spaces (,) and (,), < <, with bounded norm ‖ ‖ <, then one can use Khintchine's inequality to show that