Math Problem Statement

Use​ Euler's method to approximate the solution to the given initial value problem at the points xequals0.1​, 0.2​, 0.3​, 0.4​, and 0.5​, using steps of size 0.1 ​(hequals0.1​). StartFraction dy Over dx EndFraction equalsy left parenthesis 9 minus y right parenthesis​, ​y(0​)equals4

Solution

We are asked to use Euler's method to approximate the solution to the initial value problem:

dydx=y(9y),y(0)=4\frac{dy}{dx} = y(9 - y), \quad y(0) = 4

Euler's Method Formula

Euler's method approximates the solution to a differential equation at discrete points using the formula:

yn+1=yn+hf(xn,yn)y_{n+1} = y_n + h \cdot f(x_n, y_n)

Where:

  • yny_n is the current approximation of yy at xnx_n,
  • f(xn,yn)=dydxf(x_n, y_n) = \frac{dy}{dx} is the function describing the derivative,
  • hh is the step size.

We are given:

  • Initial value: y(0)=4y(0) = 4,
  • Step size h=0.1h = 0.1,
  • The differential equation dydx=y(9y)\frac{dy}{dx} = y(9 - y).

We'll compute yy for x=0.1,0.2,0.3,0.4,0.5x = 0.1, 0.2, 0.3, 0.4, 0.5.

Step-by-Step Calculation

  1. At x=0x = 0, y0=4y_0 = 4: f(0,4)=4(94)=4×5=20f(0, 4) = 4(9 - 4) = 4 \times 5 = 20 Euler's formula: y1=4+0.1×20=4+2=6y_1 = 4 + 0.1 \times 20 = 4 + 2 = 6 So, y(0.1)6y(0.1) \approx 6.

  2. At x=0.1x = 0.1, y1=6y_1 = 6: f(0.1,6)=6(96)=6×3=18f(0.1, 6) = 6(9 - 6) = 6 \times 3 = 18 Euler's formula: y2=6+0.1×18=6+1.8=7.8y_2 = 6 + 0.1 \times 18 = 6 + 1.8 = 7.8 So, y(0.2)7.8y(0.2) \approx 7.8.

  3. At x=0.2x = 0.2, y2=7.8y_2 = 7.8: f(0.2,7.8)=7.8(97.8)=7.8×1.2=9.36f(0.2, 7.8) = 7.8(9 - 7.8) = 7.8 \times 1.2 = 9.36 Euler's formula: y3=7.8+0.1×9.36=7.8+0.936=8.736y_3 = 7.8 + 0.1 \times 9.36 = 7.8 + 0.936 = 8.736 So, y(0.3)8.736y(0.3) \approx 8.736.

  4. At x=0.3x = 0.3, y3=8.736y_3 = 8.736: f(0.3,8.736)=8.736(98.736)=8.736×0.264=2.3059f(0.3, 8.736) = 8.736(9 - 8.736) = 8.736 \times 0.264 = 2.3059 Euler's formula: y4=8.736+0.1×2.3059=8.736+0.23059=8.96659y_4 = 8.736 + 0.1 \times 2.3059 = 8.736 + 0.23059 = 8.96659 So, y(0.4)8.96659y(0.4) \approx 8.96659.

  5. At x=0.4x = 0.4, y4=8.96659y_4 = 8.96659: f(0.4,8.96659)=8.96659(98.96659)=8.96659×0.03341=0.29965f(0.4, 8.96659) = 8.96659(9 - 8.96659) = 8.96659 \times 0.03341 = 0.29965 Euler's formula: y5=8.96659+0.1×0.29965=8.96659+0.029965=8.996555y_5 = 8.96659 + 0.1 \times 0.29965 = 8.96659 + 0.029965 = 8.996555 So, y(0.5)8.996555y(0.5) \approx 8.996555.

Final Approximation

  • y(0.1)6y(0.1) \approx 6
  • y(0.2)7.8y(0.2) \approx 7.8
  • y(0.3)8.736y(0.3) \approx 8.736
  • y(0.4)8.96659y(0.4) \approx 8.96659
  • y(0.5)8.996555y(0.5) \approx 8.996555

Would you like more details on any step?

5 Relative Questions:

  1. How does Euler's method differ from more accurate numerical methods like Runge-Kutta?
  2. What is the effect of step size hh on the accuracy of Euler’s method?
  3. Can Euler's method handle systems of differential equations, and if so, how?
  4. How would the solution change if h=0.05h = 0.05 were used instead of h=0.1h = 0.1?
  5. How is Euler’s method applied in real-world problems like population growth or physics?

Tip: Smaller step sizes in Euler's method generally yield more accurate results, but at the cost of more computation steps.

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Math Problem Analysis

Mathematical Concepts

Numerical Methods
Differential Equations
Euler's Method

Formulas

Euler's Method: y_{n+1} = y_n + h * f(x_n, y_n)
dy/dx = y(9 - y)

Theorems

Euler's method for approximating solutions to differential equations

Suitable Grade Level

College Level Calculus