quadratic polynomial


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quadratic polynomial

[kwä′drad·ik ‚päl·ə′nō·mē·əl]
(mathematics)
A polynomial where the highest degree of any of its terms is 2.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
For the mean speed of emergence (Figure 1D), all cultivars were negatively affected by the increase in salt contents, and their behavior was described by a quadratic polynomial model with maximum points of 0.21 days at ECw 1.79 dS [m.sup.-1] for Ligeirinho and 0.19 days at ECw of 1.98 dS [m.sup.-1] for Meruinho.
Caption: Figure 2: The correlation between predicted values obtained by the quadratic polynomial equations and actual experimental values.
Based on the calculated IDs, utilize the curve fitting method to obtain the quadratic polynomial coefficients.
Then we compute their inverses [L.sup.-1.sub.1] and [L.sup.-1.sub.2] and the n-variable quadratic polynomials [mathematical expression not reproducible].
The dynamics of quadratic polynomials is commonly studied by using the family of maps [f.sub.c](x) = [x.sup.2] + c, where c [member of] C and [x.sub.i+1] = [f.sub.c]([x.sub.i]) = [x.sup.2.sub.i] + c.
is hyperbolic if for every x [member of] [C.sup.n], x [not equal to] 0 the quadratic polynomial
(19) These regressions contain a quadratic polynomial of age which is fully interacted with a dummy variable indicating an age greater than or equal to the MLTPA.
The relationships between CWSI and Pn, Tr or gs could be described by quadratic polynomial equations, which were significant at pless than 0.05 confidence level.
The quadratic polynomials are used to fit the ascending and descending arcs on geodetic latitude and longitude based on the positioning information in altimetry data, respectively.
We look for a function S [member of] [C.sup.1][a,b] which is on each subinterval either a quadratic polynomial or a linear/linear rational function of the form
In fact, pixel value (L) above contains two kinds of noise: one is the noise of image itself and the other is the model error brought in by the substitution of quadratic polynomial. The model errors will depend on the distance to the center of the region using quadratic polynomial.
The most stable behavior corresponds to those ([alpha], c) values that remain in red at every parameter plane: they correspond to methods of the family that, using any free critical point as an initial estimation, it only converges to zero or infinite, that is, to any of the roots of the original quadratic polynomial. However, it can be seen at Figure 9 that all these dynamical planes are not equally useful, as the wideness of the immediate basin of attraction is clearly bigger in some cases.