quadratic function


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quadratic function

[kwə‚drad·ik ′fəŋk·shən]
(mathematics)
A function whose value is given by a quadratic polynomial in the independent variable.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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The first function is a 2D quadratic function, the graph of which is a paraboloid above a quarter of the unit circle on the x-y plane (Fig 2).
for some [epsilon] [greater than or equal to] 0 and for all x, y [member of] G, then there exists a unique quadratic function q : G [right arrow] E such that
Specific topics covered include results of Cohen-Lenstra type for quadratic function fields, constructing elliptic curves of prime order, supersingular genus-2 curves over fields of characteristic 3, and search techniques for root-unitary polynomials.
Traditionally, in the ED problem, the cost function for each generator has been approximately represented by a single quadratic function, and the valve-point effects [1] were ignored.
Consider the quadratic function f(x,c)=[chi square]+x+c of variable x with parameter c.
using an optimal fourth order symplectic integration scheme designed for separable Hamiltonian systems where the kinetic energy is a quadratic function of momentum [10].
Since all subpopulations were derived from cycle 0, the definitions of the above terms are: A0 = contribution of additive effects from Golden Glow at C0; D0 = contribution of dominance effects from Golden Glow at C0; ALI = linear function of changes in allelic frequencies influencing additive effects in subpopulation I; DLI = linear function of changes in allelic frequencies influencing dominance effects in subpopulation I; DQ I = quadratic function of changes in .
To model this curvature we use a quadratic function and allocate resources for 12 experimental runs.
The standard linear-quadratic monetary policy model assumes central bank preferences over key macroeconomic variables, such as inflation and output, can be usefully approximated by a quadratic function. This approximation implies that a deviation from a target is considered to be equally costly irrespective of whether the deviation is positive or negative.
The difference [M.sub.1](2, 1) [M.sub.1](1, 1) defines a concave quadratic function in [[alpha].sub.M].
Chapter 5 focuses on some of the results of optimization theory that apply to cases where the cost function is a convex quadratic function, but the constraints are linear.