To begin, consider an initial value problem for a linear first-order ODE. This is a linear first-order ODE. Notice that the general solution is a linear function of the ...

A plot of the solution given by DSolve can give useful information about the nature of the solution, for instance, whether it is oscillatory in nature. It can also serve as a ...

The differential equations that arise in practice are of two types. Here is an example of the first type. Here is an example of the second type. This equation has a symbolic ...

Implicit Runge–Kutta methods have a number of desirable properties. The Gauss–Legendre methods, for example, are self-adjoint, meaning that they provide the same solution ...

Here is one way to get multiple minima: call NMinimize multiple times with different random seeds, which will cause different optimization paths to be taken. This defines a ...

CUDAImageAdd[img, x] adds an amount x to each channel value in img.CUDAImageAdd[mem, x] adds an amount x to each channel value in mem.CUDAImageAdd[img 1, img 2] gives an ...

CUDAImageMultiply[img, x] multiplies an amount x to each channel value in img.CUDAImageMultiply[mem, x] multiplies an amount x to each channel value in ...

CUDAImageSubtract[img, x] subtracts an amount x from each channel value in img.CUDAImageSubtract[mem, x] subtracts an amount x from each channel value in ...

Animate[expr, {u, u_min, u_max}] generates an animation of expr in which u varies continuously from u_min to u_max. Animate[expr, {u, u_min, u_max, du}] takes u to vary in ...

EmpiricalDistribution[{x_1, x_2, ...}] represents an empirical distribution based on the data values x_i.EmpiricalDistribution[{{x_1, y_1, ...}, {x_2, y_2, ...}, ...}] ...