freeCodeCamp/curriculum/challenges/english/10-coding-interview-prep/rosetta-code/euler-method.md

title	id	challengeType	forumTopicId
Euler method	59880443fb36441083c6c20e	5	302258

Description

Euler's method numerically approximates solutions of first-order ordinary differential equations (ODEs) with a given initial value. It is an explicit method for solving initial value problems (IVPs), as described in the wikipedia page. The ODE has to be provided in the following form:

• $\frac{dy(t)}{dt} = f(t,y(t))$

with an initial value

• $y(t_0) = y_0$

To get a numeric solution, we replace the derivative on the LHS with a finite difference approximation:

• $\frac{dy(t)}{dt} \approx \frac{y(t+h)-y(t)}{h}$

then solve for $y(t+h)$:

• $y(t+h) \approx y(t) + h \, \frac{dy(t)}{dt}$

which is the same as

• $y(t+h) \approx y(t) + h \, f(t,y(t))$

The iterative solution rule is then:

• $y_{n+1} = y_n + h \, f(t_n, y_n)$

where $h$ is the step size, the most relevant parameter for accuracy of the solution. A smaller step size increases accuracy but also the computation cost, so it has always has to be hand-picked according to the problem at hand. Example: Newton's Cooling Law Newton's cooling law describes how an object of initial temperature $T(t_0) = T_0$ cools down in an environment of temperature $T_R$:

• $\frac{dT(t)}{dt} = -k \, \Delta T$

or

• $\frac{dT(t)}{dt} = -k \, (T(t) - T_R)$

It says that the cooling rate $\frac{dT(t)}{dt}$ of the object is proportional to the current temperature difference $\Delta T = (T(t) - T_R)$ to the surrounding environment. The analytical solution, which we will compare to the numerical approximation, is

• $T(t) = T_R + (T_0 - T_R) \; e^{-k t}$

Instructions

Implement a routine of Euler's method and then use it to solve the given example of Newton's cooling law for three different step sizes of:

• 2 s
• 5 s and
• 10 s

and compare with the analytical solution. Initial values:

• initial temperature $T_0$ shall be 100 °C
• room temperature $T_R$ shall be 20 °C
• cooling constant $k$ shall be 0.07
• time interval to calculate shall be from 0 s to 100 s

First parameter to the function is initial time, second parameter is initial temperature, third parameter is elapsed time and fourth parameter is step size.

Tests

tests:
  - text: <code>eulersMethod</code> should be a function.
    testString: assert(typeof eulersMethod === 'function');
  - text: <code>eulersMethod(0, 100, 100, 2)</code> should return a number.
    testString: assert(typeof eulersMethod(0, 100, 100, 2) === 'number');
  - text: <code>eulersMethod(0, 100, 100, 2)</code> should return 20.0424631833732.
    testString: assert.equal(eulersMethod(0, 100, 100, 2), 20.0424631833732);
  - text: <code>eulersMethod(0, 100, 100, 5)</code> should return 20.01449963666907.
    testString: assert.equal(eulersMethod(0, 100, 100, 5), 20.01449963666907);
  - text: <code>eulersMethod(0, 100, 100, 10)</code> should return 20.000472392.
    testString: assert.equal(eulersMethod(0, 100, 100, 10), 20.000472392);

Challenge Seed

function eulersMethod(x1, y1, x2, h) {

}

Solution

function eulersMethod(x1, y1, x2, h) {
  let x = x1;
  let y = y1;

  while ((x < x2 && x1 < x2) || (x > x2 && x1 > x2)) {
    y += h * (-0.07 * (y - 20));
    x += h;
  }

  return y;
}