ode.Rmd

---
title: "Ordinary differential equations"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Ordinary differential equations}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
knitr::opts_knit$set(
  global.par = TRUE
)
```

```{r setup}
library(calculus)
```

The function [`ode`](https://calculus.eguidotti.com/reference/ode.html) provides solvers for systems of ordinary differential equations of the type:

$$
\frac{dy}{dt} = f(t,y), \quad y(t_0)=y_0
$$

where $y$ is the vector of state variables. Two solvers are available: the simpler and faster Euler scheme^[https://en.wikipedia.org/wiki/Euler_method] or the more accurate 4-th order Runge-Kutta method^[https://en.wikipedia.org/wiki/Runge-Kutta_methods]. 
Although many packages already exist to solve ordinary differential equations in R^[https://CRAN.R-project.org/view=DifferentialEquations], they usually represent the function $f$ either with an R `function` or with `characters`. While the representation via R `functions` is usually more efficient, the symbolic representation is easier to adopt for beginners and more flexible for advanced users to handle systems that might have been generated via symbolic programming. The function [`ode`](https://calculus.eguidotti.com/reference/ode.html) supports both the representations and uses hashed `environments` to improve symbolic evaluations. 

## Examples

The vector-valued function $f$ representing the system can be specified as a vector of `characters`, or a `function` returning a numeric vector, giving the values of the derivatives at time $t$. The initial conditions are set with the argument `var` and the time variable can be specified with `timevar`.

```{r, echo=FALSE}
par(mar = c(4, 4, 1, 1))
```

### Symbolic system 
$$
\frac{dx}{dt}=x, \quad x_0 = 1
$$

```{r}
f <- "x"
var <- c(x=1)
times <- seq(0, 2*pi, by=0.001)
x <- ode(f = f, var = var, times = times)
plot(times, x, type = "l")
```

### Time dependent system

$$
\frac{dx}{dt}=\cos(t), \quad x_0 = 0
$$

```{r}
f <- "cos(t)"
var <- c(x=0)
times <- seq(0, 2*pi, by=0.001)
x <- ode(f = f, var = var, times = times, timevar = "t")
plot(times, x, type = "l")
```

### Multivariate system

$$
\frac{d}{dt}
\begin{bmatrix}
x\\
y
\end{bmatrix}=
\begin{bmatrix}
x\\
x(1+\cos(10t))
\end{bmatrix}, \quad
\begin{bmatrix}
x_0\\y_0
\end{bmatrix}=
\begin{bmatrix}
1\\1
\end{bmatrix}
$$

```{r}
f <- c("x", "x*(1+cos(10*t))")
var <- c(x=1, y=1)
times <- seq(0, 2*pi, by=0.001)
x <- ode(f = f, var = var, times = times, timevar = "t")
matplot(times, x, type = "l", lty = 1, col = 1:2)
```

### Numerical system

$$
\frac{d}{dt}
\begin{bmatrix}
x\\
y
\end{bmatrix}=
\begin{bmatrix}
x\\
y
\end{bmatrix}, \quad
\begin{bmatrix}
x_0\\y_0
\end{bmatrix}=
\begin{bmatrix}
1\\2
\end{bmatrix}
$$
```{r}
f <- function(x, y) c(x, y)
var <- c(x=1, y=2)
times <- seq(0, 2*pi, by=0.001)
x <- ode(f = f, var = var, times = times)
matplot(times, x, type = "l", lty = 1, col = 1:2)
```

### Vectorized interface

$$
\frac{d}{dt}
\begin{bmatrix}
x\\
y\\
z
\end{bmatrix}=
\begin{bmatrix}
x\\
y\\
y*(1+cos(10*t))
\end{bmatrix}, \quad
\begin{bmatrix}
x_0\\y_0\\z_0
\end{bmatrix}=
\begin{bmatrix}
1\\2\\2
\end{bmatrix}
$$

```{r}
f <- function(x, t) c(x[1], x[2], x[2]*(1+cos(10*t)))
var <- c(1,2,2)
times <- seq(0, 2*pi, by=0.001)
x <- ode(f = f, var = var, times = times, timevar = "t")
matplot(times, x, type = "l", lty = 1, col = 1:3)
```

## Cite as

Guidotti E (2022). “calculus: High-Dimensional Numerical and Symbolic Calculus in R.” _Journal of Statistical Software_, *104*(5), 1-37. [doi:10.18637/jss.v104.i05](https://doi.org/10.18637/jss.v104.i05)

A BibTeX entry for LaTeX users is

```bibtex
@Article{calculus,
  title = {{calculus}: High-Dimensional Numerical and Symbolic Calculus in {R}},
  author = {Emanuele Guidotti},
  journal = {Journal of Statistical Software},
  year = {2022},
  volume = {104},
  number = {5},
  pages = {1--37},
  doi = {10.18637/jss.v104.i05},
}
```