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OR682/Math685/CSI700. Lecture 5 Fall 2000. Nonlinear Equations. Solving f ( x ) = 0 (1 equation, n equations) Assume that [# of equations] = [# of variables] Closely related to: minimize F ( x ) Solve: F ( x ) = 0

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Nonlinear Equations

- Solving f (x) = 0 (1 equation, n equations)
- Assume that [# of equations] = [# of variables]
- Closely related to: minimize F (x)
- Solve: F(x) = 0
- “Always” better to use optimization software to solve optimization problems

- Applications:
- Nonlinear differential equations
- Design of integrated circuits
- Data fitting with nonlinear models (e.g., exponential terms)

Examples

- 1-variable: x2 = 4 sin(x)
- 2-variable:

Solutions of Nonlinear Equations

- Nonlinear equations can have any number of solutions:
- No solution: exp(x) + 1 = 0
- 1 solution: exp(–x) – x = 0
- 2 solutions: x2 – 4 sin(x) = 0
- Infinitely many solutions: sin(x) = 0

- Iterative methods are necessary: no general exact formulas exist, even for polynomials
- Terminology: solution = root = zero

Multiple Roots

- A nonlinear equation can have a multiple root: f (x) = 0 and f(x) = 0
- Examples: (x – 1)k = 0
- It is impossible to determine a multiple root to full machine accuracy
- It is harder computationally to determine a multiple root, especially one with even multiplicity

Accuracy of Solutions

- We can measure if the residual is small:
- Or if the error is small (x* is solution):
- These are related, but not equivalent

Conditioning

- Mathematically: x* = f –1(0)
- If computing f (x) is insensitive, then computing the root is sensitive
- If computing f (x) is sensitive, then computing the root is insensitive
- If we define F( y) f –1( y) thenF (0) = 1 / f (x*)

Convergence Rate

- Measuring speed of an iterative method
- Define error: ek = xk– x*
- For some algorithms, error will be the length of an interval containing x*

- The sequence converges to zero with rate r if:

Convergence Rate (continued)

- Some important cases:
- Linear (r = 1): requires C < 1
- Superlinear (r > 1): # of digits gained per iteration increases at each iteration
- Quadratic (r = 2): # of accurate digits doubles at each iteration

- Convergence rates refer to asymptotic behavior (close to the solution); early iterations of the algorithm may produce little progress

Bisection: Simple & Safe

- Require [a,b] with f (a) f (b) < 0
- Reduce interval until error is “small”
- While ((b – a) > tol1)
Compute midpoint m = a + (b– a)/2

If | f (m)| < tol2, stop

If f (a) f (m) < 0 then b = m, else a = m

end

Matlab m-files: bisect.m

Bisection, Continued

- Interval reduced by ½ each iteration
- Linear convergence (r = 1, C = ½)
- Bisection approximates f (x) by the line through [a,sign( f (a))] and [b,sign( f (b))] and determines the point m where this line is zero
- This is a crude model of f (x)
- What about multiple roots?

Matlab m-files: bisect_model.m

Newton’s Method

- Approximate f (x) by its Taylor series:
- Find point where line is zero:
- Repeat this computation to get Newton’s method:

Matlab m-files: newton_model.m, newton.m

Newton’s Method: Convergence

- Note: ek = xk– x* so x* = xk – ek. Thus
- Quadratic convergence (r = 2) if f(x*) 0

Secant Method

- Goal: reduce iteration cost of Newton’s method
- Approximate f(x) by finite difference:
- Superlinear convergence (r 1.6)

Safeguarded Methods

- Newton, secant methods:
- Fast close to solution
- Potentially unreliable (esp. away from solution)

- Bisection (and other) methods:
- Slow to converge
- Reliable

- Safeguarded method:
- Monitor performance of fast method
- Use slow, safe method to guarantee convergence
- Near solution, the slow method usually not needed

Systems of Nonlinear Equations

- Much more difficult than scalar case
- Theoretical analysis harder, behavior of roots potentially stranger
- No absolutely safe, reliable method
- Costs rise rapidly with # of variables
- Can only guarantee that algorithm converges to a solution of:

Newton’s Method

- In n dimensions:
where (J = Jacobian matrix)

- Quadratic convergence rate (if assumptions satisfied)

Matlab m_files: newton_s.m

Newton’s Method (continued)

- Computational costs
- O(n2) to compute Jacobian
- O(n3) to solve Newton equations

- Alternative methods
- Analogs of secant method

- Safeguards
- Essential to guarantee convergence
- “line search” or “trust region”

Matlab Software

- 1-variable: fzero
- n-variable: fsolve

For Next Class

- Homework: see web site
- Reading:
- Heath: chapter 7

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