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3.
Secant Method – Derivation http://numericalmethods.eng.usf.edu Newton’s Method Approximate the derivative Substituting Equation (2) into Equation (1) gives the Secant method (1) (2) Figure 1 Geometrical illustration of the Newton-Raphson method.
4.
Secant Method – Derivation http://numericalmethods.eng.usf.edu The Geometric Similar Triangles Figure 2 Geometrical representation of the Secant method. The secant method can also be derived from geometry: can be written as On rearranging, the secant method is given as
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Algorithm for Secant Method http://numericalmethods.eng.usf.edu
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Step 1 http://numericalmethods.eng.usf.edu Calculate the next estimate of the root from two initial guesses Find the absolute relative approximate error
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Step 2
Find if the absolute relative approximate error is greater than the prespecified relative error tolerance.
If so, go back to step 1, else stop the algorithm.
Also check if the number of iterations has exceeded the maximum number of iterations.
http://numericalmethods.eng.usf.edu
8.
Example 1
You are making a bookshelf to carry books that range from 8 ½ ” to 11” in height and would take 29”of space along length. The material is wood having Young’s Modulus 3.667 Msi, thickness 3/8 ” and width 12”. You want to find the maximum vertical deflection of the bookshelf. The vertical deflection of the shelf is given by
http://numericalmethods.eng.usf.edu where x is the position where the deflection is maximum. Hence to find the maximum deflection we need to find where and conduct the second derivative test.
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Example 1 Cont.
The equation that gives the position x where the deflection is maximum is given by
http://numericalmethods.eng.usf.edu Use the secant method of finding roots of equations to find the position where the deflection is maximum. Conduct three iterations to estimate the root of the above equation. Find the absolute relative approximate error at the end of each iteration and the number of significant digits at least correct at the end of each iteration. Figure 2 A loaded bookshelf.
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Example 1 Cont. http://numericalmethods.eng.usf.edu Figure 3 Graph of the function f(x).
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Example 1 Cont.
Let us take the initial guesses of the root of as and .
Iteration 1
The estimate of the root is
http://numericalmethods.eng.usf.edu Solution
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Example 1 Cont. http://numericalmethods.eng.usf.edu Figure 4 Graph of the estimated root after Iteration 1.
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Example 1 Cont.
The absolute relative approximate error at the end of Iteration 1 is
http://numericalmethods.eng.usf.edu The number of significant digits at least correct is 1, because the absolute relative approximate error is less than 5%.
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Example 1 Cont.
Iteration 2
The estimate of the root is
http://numericalmethods.eng.usf.edu
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Example 1 Cont. http://numericalmethods.eng.usf.edu Figure 5 Graph of the estimate root after Iteration 2.
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Example 1 Cont.
The absolute relative approximate error at the end of Iteration 2 is
http://numericalmethods.eng.usf.edu The number of significant digits at least correct is 2, because the absolute relative approximate error is less than 0.5%.
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Example 1 Cont.
Iteration 3
The estimate of the root is
http://numericalmethods.eng.usf.edu
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Example 1 Cont. http://numericalmethods.eng.usf.edu Figure 6 Graph of the estimate root after Iteration 3.
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Example 1 Cont.
The absolute relative approximate error at the end of Iteration 3 is
http://numericalmethods.eng.usf.edu The number of significant digits at least correct is 6, because the absolute relative approximate error is less than 0.00005%.
20.
Advantages
Converges fast, if it converges
Requires two guesses that do not need to bracket the root
http://numericalmethods.eng.usf.edu
21.
Drawbacks http://numericalmethods.eng.usf.edu Division by zero
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