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  2. 2004 Monaco Grand Prix - Wikipedia

    en.wikipedia.org/wiki/2004_Monaco_Grand_Prix

    The 2004 Monaco Grand Prix (formally the Formula 1 Grand Prix de Monaco 2004) [1] was a Formula One motor race held on 23 May 2004, at the Circuit de Monaco, contested over 77 laps. It was Race 6 of 18 in the 2004 FIA Formula One World Championship. The race was won by Renault driver Jarno Trulli; his only victory in Formula One.

  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. Sylvester's formula - Wikipedia

    en.wikipedia.org/wiki/Sylvester's_formula

    In matrix theory, Sylvester's formula or Sylvester's matrix theorem (named after J. J. Sylvester) or Lagrange−Sylvester interpolation expresses an analytic function f(A) of a matrix A as a polynomial in A, in terms of the eigenvalues and eigenvectors of A. [1] [2] It states that [3]

  5. Inequation - Wikipedia

    en.wikipedia.org/wiki/Inequation

    Similar to equation solving, inequation solving means finding what values (numbers, functions, sets, etc.) fulfill a condition stated in the form of an inequation or a conjunction of several inequations. These expressions contain one or more unknowns, which are free variables for which values are sought that cause the condition to be fulfilled ...

  6. Sum-of-squares optimization - Wikipedia

    en.wikipedia.org/wiki/Sum-of-Squares_Optimization

    A sum-of-squares optimization program is an optimization problem with a linear cost function and a particular type of constraint on the decision variables. These constraints are of the form that when the decision variables are used as coefficients in certain polynomials, those polynomials should have the polynomial SOS property.

  7. Linear matrix inequality - Wikipedia

    en.wikipedia.org/wiki/Linear_matrix_inequality

    In convex optimization, a linear matrix inequality (LMI) is an expression of the form ⁡ ():= + + + + where = [, =, …,] is a real vector,,,, …, are symmetric matrices, is a generalized inequality meaning is a positive semidefinite matrix belonging to the positive semidefinite cone + in the subspace of symmetric matrices .

  8. Fourier–Motzkin elimination - Wikipedia

    en.wikipedia.org/wiki/Fourier–Motzkin_elimination

    Since all the inequalities are in the same form (all less-than or all greater-than), we can examine the coefficient signs for each variable. Eliminating x would yield 2*2 = 4 inequalities on the remaining variables, and so would eliminating y. Eliminating z would yield only 3*1 = 3 inequalities so we use that instead.

  9. Max–min inequality - Wikipedia

    en.wikipedia.org/wiki/Max–min_inequality

    In mathematics, the max–min inequality is as follows: For any function f : Z × W → R , {\displaystyle \ f:Z\times W\to \mathbb {R} \ ,} sup z ∈ Z inf w ∈ W f ( z , w ) ≤ inf w ∈ W sup z ∈ Z f ( z , w ) . {\displaystyle \sup _{z\in Z}\inf _{w\in W}f(z,w)\leq \inf _{w\in W}\sup _{z\in Z}f(z,w)\ .}