Skip to main content

calculus and analysis - Equation of a line that is tangent to a curve at point


A common problem in the derivative section of calculus texts is "find the equation of the line that is tangent to the curve $y = \ldots$ at the point $P$."


To find the line that is tangent to $y = 2 x \sin x$ at $(\pi/2,\pi)$, I'd do something like this in Mathematica:


y[x_] := 2 x Sin[x]
y[x] == y'[x] x + b /. x -> Pi/2;
bRule = Solve[%, b][[1]];

y == y'[a] x + b /. bRule /. a -> Pi/2

which outputs y == 2 x.


Is this more or less idiomatic Mathematica code? Is there a better way?



Answer



Certainly, there is a better way:


y[x_] := 2 x Sin[x]; a = Pi/2;
Collect[Normal[Series[y[x], {x, a, 1}]], x, Simplify]

Recall that the formula for a Taylor polynomial looks a bit like this:



$$f(x)=\color{red}{f(a)+f^\prime (a)(x-a)}+\frac{f^{\prime\prime}(a)}{2}(x-a)^2+\cdots$$


and reconciling this with the geometric interpretation of the Taylor polynomial as the best one-point osculatory (agrees at function and derivative values) approximation of a function shows why the approach works. I believe this should be a standard way to look at Taylor polynomials in the textbooks, if it already isn't.




Here is an equivalent approach:


Collect[InterpolatingPolynomial[{{{a}, y[a], y'[a]}}, x], x, Simplify]

This is based on the fact that the tangent line is the unique Hermite interpolating polynomial of degree $1$.




Certainly, one could do the plodding, "traditional" (whatever that means) approach:


y[x_] := 2 x Sin[x]; a = Pi/2;

Collect[y[a] + y'[a] (x - a), x, Simplify]

In any event, Solve[] is definitely unnecessary here.


Comments

Popular posts from this blog

plotting - Plot 4D data with color as 4th dimension

I have a list of 4D data (x position, y position, amplitude, wavelength). I want to plot x, y, and amplitude on a 3D plot and have the color of the points correspond to the wavelength. I have seen many examples using functions to define color but my wavelength cannot be expressed by an analytic function. Is there a simple way to do this? Answer Here a another possible way to visualize 4D data: data = Flatten[Table[{x, y, x^2 + y^2, Sin[x - y]}, {x, -Pi, Pi,Pi/10}, {y,-Pi,Pi, Pi/10}], 1]; You can use the function Point along with VertexColors . Now the points are places using the first three elements and the color is determined by the fourth. In this case I used Hue, but you can use whatever you prefer. Graphics3D[ Point[data[[All, 1 ;; 3]], VertexColors -> Hue /@ data[[All, 4]]], Axes -> True, BoxRatios -> {1, 1, 1/GoldenRatio}]

plotting - Filling between two spheres in SphericalPlot3D

Manipulate[ SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, Mesh -> None, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], {n, 0, 1}] I cant' seem to be able to make a filling between two spheres. I've already tried the obvious Filling -> {1 -> {2}} but Mathematica doesn't seem to like that option. Is there any easy way around this or ... Answer There is no built-in filling in SphericalPlot3D . One option is to use ParametricPlot3D to draw the surfaces between the two shells: Manipulate[ Show[SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], ParametricPlot3D[{ r {Sin[t] Cos[1.5 Pi], Sin[t] Sin[1.5 Pi], Cos[t]}, r {Sin[t] Cos[0 Pi], Sin[t] Sin[0 Pi], Cos[t]}}, {r, 1, 2 - n}, {t, 0, Pi}, PlotStyle -> Yellow, Mesh -> {2, 15}]], {n, 0, 1}]

plotting - Mathematica: 3D plot based on combined 2D graphs

I have several sigmoidal fits to 3 different datasets, with mean fit predictions plus the 95% confidence limits (not symmetrical around the mean) and the actual data. I would now like to show these different 2D plots projected in 3D as in but then using proper perspective. In the link here they give some solutions to combine the plots using isometric perspective, but I would like to use proper 3 point perspective. Any thoughts? Also any way to show the mean points per time point for each series plus or minus the standard error on the mean would be cool too, either using points+vertical bars, or using spheres plus tubes. Below are some test data and the fit function I am using. Note that I am working on a logit(proportion) scale and that the final vertical scale is Log10(percentage). (* some test data *) data = Table[Null, {i, 4}]; data[[1]] = {{1, -5.8}, {2, -5.4}, {3, -0.8}, {4, -0.2}, {5, 4.6}, {1, -6.4}, {2, -5.6}, {3, -0.7}, {4, 0.04}, {5, 1.0}, {1, -6.8}, {2, -4.7}, {3, -1....