Examples of using Linear approximation in English and their translations into Greek
{-}
-
Colloquial
-
Official
-
Medicine
-
Ecclesiastic
-
Financial
-
Official/political
-
Computer
Is the best linear approximation to f at a.
The common thread is that the derivative of a function at a point serves as a linear approximation of the function at that point.
This is only a linear approximation of the instantaneous change, which requires calculus to solve.
I finally figured out the linear approximations.
Taking the best linear approximation in a single direction determines a partial derivative, which is usually denoted∂y/∂x.
However, for weak fields, a linear approximation can be made.
Which has the intuitive interpretation(see Figure 1)that the tangent line to at gives the best linear approximation.
Examples with detailed solutions on linear approximations are presented.
Assume that the error in these linear approximation formula is bounded by a constant times||v||, where the constant is independent of v but depends continuously on a.
That is, for any vector v starting at a, the linear approximation formula holds.
For the linear approximation formula to make sense, f′(a) must be a function that sends vectors in Rn to vectors in Rm, and f′(a)v must denote this function evaluated at v.
To determine what kind of function it is,notice that the linear approximation formula can be rewritten as.
Since the derivative is the slope of the linear approximation to f at the point a, the derivative(together with the valueof f at a) determines the best linear approximation, or linearization, of f near the point a.
The derivative of a function at a chosen input value describes the best linear approximation of the function near that input value.
That is, for any vector v starting at a, the linear approximation formula holds: f( a+ v)≈ f( a)+ f′( a) v.{\displaystyle f(\mathbf{a}+\mathbf{v})\approx f(\mathbf{a})+f'(\mathbf{a})\mathbf{ v}.} Just like the single-variable derivative, f′(a) is chosen so that the error in this approximation is as small as possible.
In this case, the linear map described by Jf(p)is the best linear approximation of f near the point p, in the sense that.
If x andy are vectors, then the best linear approximation to the graph of f depends on how f changes in several directions at once.
For a real-valued function of a real variable,the derivative of a function at a point generally determines the best linear approximation to the function at that point.
Assume that the error in these linear approximation formula is bounded by a constant times||v||.
When f is a function from an open subset of Rn to Rm,then the directional derivative of f in a chosen direction is the best linear approximation to f at that….
In one variable,the fact that the derivative is the best linear approximation is expressed by the fact that it is the limit of difference quotients.
The importance of the Jacobian lies in the fact that it represents the best linear approximation to a differentiable function near a given point.
The derivative gives the best possible linear approximation of a function at a given point, but this can be very different from the original function.
The Generalized Reduced Gradient method used by Microsoft Excel Solver is quite efficient for problems of this type because it uses linear approximations to the problem functions at a number of stages in the solution process; when the actual functions are linear, these approximations are exact.
To make precisethe idea that f′(a) is the best linear approximation, it is necessary to adapt a different formula for the one-variable derivative in which these problems disappear.
With GN Dashboard you can solve various engineering problems from classic regression and approximation to linear programming transportation and location problems and other machine learning based problems.