Pt1420 Unit 8 Assignment

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Table of Contents Numerical Integration 2 Trapezoidal Rule 2 Simpson’s Rule 3 Roots of Equation: 4 Fixed‐Point Iteration 4 Newton‐Raphson Method 4 Systems of Linear Equations 4 LU Decomposition 4 Gauss‐Seidel 4 References: 4 Numerical Integration Numerical integration consist of a wide variety of different method for calculating the area under the curve. Some of the ones that I will cover in this portfolio are the Trapezoidal Rule and the Simpson 1/3 Rule. I will explain how some of these algorithms are used in relation to the numerical integration. Trapezoidal Rule The trapezoidal rule is used to approximate the area under a curve. The approximation uses the area of the trapezoid,h/2(p+q), to find area where the …show more content…

The fact that an infinitesimal number of trapezoids can give you the area under any curve it makes it an important concept. Understanding the Trapezoidal Rule will make understanding integration much easier. The Trapezoidal Rule has many applications in real word problem solving. It is used to weather forecasting and determining how much rainfall is going to fall in a certain part of town. It is a rough estimation of the amount of rainfall. Simpson’s Rule The Simpson Rule is used to approximate the area under a curve. This time instead of using trapezoids to approximate the area under the curve, we use parabolas. The parabolas will help us get a more precise approximation of the area under the curve because we will not have the void space that was caused by trapezoid. The Trapezoidal Rule was the approximated using a first order polynomial were as the Simpson Rule is approximated using a second order polynomial. The formal for the Simpson’s Rule would go as …show more content…

The Newton-Raphson method uses the same principles of the Fixed-Point Iteration with respects that it starts with an initial approximation and then the sequence is generated through iterations from the initial base point. The general form of the function looks as follows: g(x) =x-f(x)/(f^' (x) ) , where the sequence is generated by p_n =g(p_(n-1)) This function will replace the f by the tangent that was estimate by pn value. The function finds the upper and the lower limit and interpolates where the point in between will fall. The root of the equation will be given by this number. Figure 6 shows how the interpolation of the middle value is found by means of the tangent line. The tangent line, in red, is fixed at a point and uses the upper and lower limits to find a single iteration. The Newton-Raphson Method has many applications in approximating the root. When comparing the Newton-Raphson Method with the Fixed-Point Iteration, the best to use would be the Newton-Raphson Method because it will be able to iterate through any function a lot faster. Using the same function, e-x, the Newton-Raphson Method would be able to find the root within 8 iterations while the Fixed-Point Iteration would take 20 iterations to get to same

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