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trace mode: cross-hair following plot, coordinates shown in the status bar; zooming support; ability to draw the 1st and 2nd derivative and the integral of a plot function; support user-defined constants and parameter values; various tools for plot functions: find minimum/maximum point, get y-value and draw the area between the function and the ...
Given a function: from a set X (the domain) to a set Y (the codomain), the graph of the function is the set [4] = {(, ()):}, which is a subset of the Cartesian product.In the definition of a function in terms of set theory, it is common to identify a function with its graph, although, formally, a function is formed by the triple consisting of its domain, its codomain and its graph.
Typically, star plots are generated in a multi-plot format with many stars on each page and each star representing one observation. Surface plot : In this visualization of the graph of a bivariate function, a surface is plotted to fit a set of data triplets (X, Y, Z), where Z if obtained by the function to be plotted Z=f(X, Y). Usually, the set ...
If f : X → Y is an ordinary function, then its inverse is the multivalued function defined as Γ f, viewed as a subset of X × Y. When f is a differentiable function between manifolds, the inverse function theorem gives conditions for this to be single-valued locally in X.
A log–log plot of y = x (blue), y = x 2 (green), and y = x 3 (red). Note the logarithmic scale markings on each of the axes, and that the log x and log y axes (where the logarithms are 0) are where x and y themselves are 1. Comparison of linear, concave, and convex functions when plotted using a linear scale (left) or a log scale (right).
The function does not include the origin (x, y) = (0, 0), if it did then f would be ill-defined at that point. Using a 3d Cartesian coordinate system with the xy-plane as the domain R 2, and the z axis the codomain R, the image can be visualized as a curved surface. The function can be evaluated at the point (x, y) = (2, √ 3) in X:
Interpolation is a common way to approximate functions. Given a function : [,] with a set of points ,, …, [,] one can form a function : [,] such that () = for =,, …, (that is, that interpolates at these points). In general, an interpolant need not be a good approximation, but there are well known and often reasonable conditions where it will.
In numerical analysis, multivariate interpolation is interpolation on functions of more than one variable [1] (multivariate functions); when the variates are spatial coordinates, it is also known as spatial interpolation. The function to be interpolated is known at given points (,,, …