Examples of using Linear approximation in English and their translations into Ukrainian
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Is the linear approximation of f at the point a.
This is a natural inverse of the linear approximation to tetration.
This is a linear approximation taken from satellite data on our Earth.
If the temperature Tdoes not vary too much, a linear approximation is typically used:.
In the linear approximation for this transport of angular momentum, the rate of flow is written:[1]: p.308.
Graph of f(x)= ex(blue) with its linear approximation P1(x)= 1+ x(red) at a= 0.
The linear approximation to tetration, on the other hand, had been known before, for example by Ioannis Galidakis.
The method of using linear approximation is more suitable.
If a real-valued function fis differentiable at the point a then it has a linear approximation at the point a.
Because the linear approximation is only an approximation, α{\displaystyle\alpha} is different for different reference temperatures.
These are not constant, so I provide a first-order(linear) approximation of the secular change:.
Taylor's theorem ensures that the quadratic approximation is, in a sufficiently small neighborhood of the point a,a better approximation than the linear approximation. .
For example, it can be used withlinear algebra to find the"best fit" linear approximation for a set of points in a domain.
Thus, for the purpose of representing large numbers, the linear approximation approach provides enough continuity( C 0{\displaystyle C^{0}} continuity) to ensure that all real numbers can be represented on a super-logarithmic scale.
A comparative analysis of the proposed methodology hasbeen made to the previously developed method of linear approximation of required functions.
Just as the first derivative is related to linear approximations, the second derivative is related to the best quadratic approximation for a function f.
The ring-diagram approximation for a partition function of relativistis system of the charged particles,which is described by the Hamiltonian in the linear approximation in the coupling constant.
These approaches are as follows: The linear approximation approach by Rubstov and Romerio, The quadratic approximation approach by Andrew Robbins, The regular Abel function approach by George Szekeres, The iterative functional approach by Peter Walker, and The natural matrix approach by Peter Walker, and later generalized by Andrew Robbins.
The partition function of relativistic system of charged particles is studied in the ring-diagramapproximation on the base of the Hamiltonian which is obtained in the linear approximation in the coupling constant.
The viscous stress tensor is only a linear approximation of the stresses around a point p, and does not account for higher-order terms of its Taylor series. However in almost all practical situations these terms can be ignored, since they become negligible at the size scales where the viscous stress is generated and affects the motion of the medium.
For this reason it is usual to specify the temperature that α{\displaystyle\alpha} was measured at with a suffix, such as α 15{\displaystyle\alpha_{15}}, and the relationship only holds in a range of temperatures aroundthe reference.[46] When the temperature varies over a large temperature range, the linear approximation is inadequate and a more detailed analysis and understanding should be used.
However, linear function approximation is not the only choice.
Greedy GQ is a variant of Q-learning to use in combination with(linear) function approximation.
The proposed approachenables one to obtain solutions of problems of classical approximation theory in abstract linear spaces in explicit form.
We determine the exact value of the best linear polynomial approximation of a unit ball of the Hardy space$H_p, 1≤ p≤∞$, on concentric circles$Tρ= z∈ C:|z|.
We propose an approach that enables one to pose andcompletely solve main extremal problems in approximation theory in abstract linear spaces.
In this approximation, trigonometric functions can be expressed as linear functions of the angles.
Convex optimization has applications in a wide range of disciplines, such as automatic control systems, estimation and signal processing, communications and networks, electronic circuit design,[5] data analysis and modeling, finance, statistics(optimal experimental design),[6] and structural optimization,where the approximation concept has proven to be efficient.[7][8] With recent advancements in computing and optimization algorithms, convex programming is nearly as straightforward as linear programming.[9].
We construct the best linear method of approximation and prove the uniqueness of this method.