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**Lecture 5 Newton-Raphson Method**

Assumptions Interpretation Examples Convergence Analysis Reading Assignment: Sections 6.2 EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Root finding Problems Many problems in Science and Engineering are expressed as These problems are called root finding problems EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Solution Methods Several ways to solve nonlinear equations are possible. Analytical Solutions possible for special equations only Graphical Solutions Useful for providing initial guesses for other methods Numerical Solutions Open methods Bracketing methods EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Bracketing Methods In bracketing methods, the method starts with an interval that contains the root and a procedure is used to obtain a smaller interval containing the root. Examples of bracketing methods : Bisection method False position method EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Open Methods In the open methods, the method starts with one or more initial guess points. In each iteration a new guess of the root is obtained. Open methods are usually more efficient than bracketing methods They may not converge to the root. EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**These will be covered in EE3561**

Solution Methods Many methods are available to solve nonlinear equations Bisection Method Newton’s Method Secant Method False position Method Muller’s Method Bairstow’s Method Fixed point iterations ………. These will be covered in EE3561 EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Newton-Raphson Method (also known as Newton’s Method)**

Given an initial guess of the root x0 , Newton-Raphson method uses information about the function and its derivative at that point to find a better guess of the root. Assumptions: f (x) is continuous and first derivative is known An initial guess x0 such that f ’(x0) ≠0 is given EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Newton’s Method F.m FP.m EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Example EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Example Iteration xk f(xk) f’(xk) xk+1 |xk+1 –xk| 4 33 3 1 9 16 2.4375**

4 33 3 1 9 16 2.4375 0.5625 2 2.0369 9.0742 2.2130 0.2245 0.2564 6.8404 2.1756 0.0384 0.0065 6.4969 2.1746 0.0010 EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Convergence analysis of the Newton’s Method**

The Taylor series expansion can be represented as An approximate solution can be obtained by At the intersection with x axis f(xi+1)=0, which gives EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Convergence analysis of the Newton’s Method**

Substituting the true value xr into Eq (B6.2.1) yields Subtracting Eq (B6.2.2) from Eq (B6.2.3) yields Substituting and gives EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Convergence analysis of the Newton’s Method**

If we assume convergence, both xi and should eventually approximated by the root xr, and Eq(B6.2.5) can be rearranged to yield Which means that the current error is proportional to the square of the previous error which indicates quadratic convergence rate. EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Convergence Analysis Remarks**

When the guess is close enough to a simple root of the function then Newton’s method is guaranteed to converge quadratically. Quadratic convergence means that the number of correct digits is nearly doubled at each iteration. EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Problems with Newton’s Method**

If the initial guess of the root is far from the root the method may not converge. Newton’s method converges linearly near multiple zeros { f(r) = f ’(r) =0 }. In such a case modified algorithms can be used to regain the quadratic convergence. EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Multiple Roots EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Problems with Newton’s Method Runaway**

4/10/2017 Problems with Newton’s Method Runaway x0 x1 The estimates of the root is sent away from the root because a near-zero slope is reached The estimates of the root is going away from the root. EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Problems with Newton’s Method Flat Spot**

4/10/2017 Problems with Newton’s Method Flat Spot x0 A zero slope causes division by zero which means that the solution soots off horizontally and never hits the x axis The value of f’(x) is zero, the algorithm fails. If f ’(x) is very small then x1 will be very far from x0. EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Problems with Newton’s Method Cycle**

4/10/2017 Problems with Newton’s Method Cycle x1=x3=x5 x0=x2=x4 Cycling between two values happens when an inflection point (f’’(x)=0) occurs in the vicinity of a root. The algorithm cycles between two values x0 and x1 EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Newton’s Method for Systems of nonlinear Equations**

4/10/2017 Newton’s Method for Systems of nonlinear Equations Newton’s Method for Systems of nonlinear Equations The Newton’s method can be extended to solve system of non-linear equations. To be able to solve systems of non-linear equations, you must be familiar with the following. Computing derivatives of vector valued functions Basic matrix calculations (addition, multiplication, determinant,…) Computing inverse of square matrices EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Example Solve the following system of equations EE3561_Unit 2**

(c)AL-DHAIFALLAH1435

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**Solution Using Newton’s Method**

EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Example Try this Solve the following system of equations EE3561_Unit 2**

(c)AL-DHAIFALLAH1435

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Example Solution EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Secant Method Examples Convergence Analysis**

EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Newton’s Method (Review)**

EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Secant Method EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Secant Method EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Secant Method EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Secant Method Yes NO Stop EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Modified Secant Method**

4/10/2017 Modified Secant Method EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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4/10/2017 Example EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Example x(i) f( x(i) ) x(i+1) |x(i+1)-x(i)| -1.0000 1.0000 -1.1000**

0.0585 0.0102 0.0009 0.0001 0.0000 EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Convergence Analysis The rate of convergence of the Secant method is super linear It is better than Bisection method but not as good as Newton’s method EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Comparison of Root finding methods**

Advantages/disadvantages Examples EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Summary Bisection Newton Secant Reliable, Slow**

One function evaluation per iteration Needs an interval [a,b] containing the root, f(a) f(b)<0 No knowledge of derivative is needed Newton Fast (if near the root) but may diverge Two function evaluation per iteration Needs derivative and an initial guess x0, f ’ (x0) is nonzero Secant Fast (slower than Newton) but may diverge one function evaluation per iteration Needs two initial points guess x0, x1 such that f (x0)- f (x1) is nonzero. EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Example EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Solution _______________________________ k xk f(xk)**

________________________________ EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Example EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Five iterations of the solution**

k xk f(xk) f’(xk) ERROR ______________________________________ EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Example EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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Example EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Example Estimates of the root of x-cos(x)=0**

initial guess correct digit correct digits correct digits correct digits EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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**Example In estimating the root of x-cos(x)=0**

To get more than 13 correct digits 4 iterations of Newton (x0=0.6) 43 iterations of Bisection method (initial interval [0.6, .8] 5 iterations of Secant method ( x0=0.6, x1=0.8) EE3561_Unit 2 (c)AL-DHAIFALLAH1435

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