Scientific Computing: An Introductory Survey

Transcription

1 Scientific Computing: An Introductory Survey Chapter 5 Nonlinear Equations Prof. Michael T. Heath Department of Computer Science University of Illinois at Urbana-Champaign Copyright c Reproduction permitted for noncommercial, educational use only. Selected subset of slides for CS page numbers will not necessarily be correct Michael T. Heath Scientific Computing 1 / 55

2 Outline 1 Nonlinear Equations 2 3 Michael T. Heath Scientific Computing 2 / 55

3 Nonlinear Equations Nonlinear Equations Solutions and Sensitivity Convergence Given function f, we seek value x for which f(x) = 0 Solution x is root of equation, or zero of function f So problem is known as root finding or zero finding Michael T. Heath Scientific Computing 3 / 55

4 Nonlinear Equations Nonlinear Equations Solutions and Sensitivity Convergence Two important cases Single nonlinear equation in one unknown, where f : R R Solution is scalar x for which f(x) = 0 System of n coupled nonlinear equations in n unknowns, where f : R n R n Solution is vector x for which all components of f are zero simultaneously, f(x) = 0 Michael T. Heath Scientific Computing 4 / 55

5 Examples: Nonlinear Equations Nonlinear Equations Solutions and Sensitivity Convergence Example of nonlinear equation in one dimension x 2 4 sin(x) = 0 for which x = 1.9 is one approximate solution Example of system of nonlinear equations in two dimensions x 2 1 x = 0 x 1 + x = 0 for which x = [ ] T is solution vector Michael T. Heath Scientific Computing 5 / 55

6 Existence and Uniqueness Nonlinear Equations Solutions and Sensitivity Convergence Existence and uniqueness of solutions are more complicated for nonlinear equations than for linear equations For function f : R R, bracket is interval [a, b] for which sign of f differs at endpoints If f is continuous and sign(f(a)) sign(f(b)), then Intermediate Value Theorem implies there is x [a, b] such that f(x ) = 0 There is no simple analog for n dimensions Michael T. Heath Scientific Computing 6 / 55

7 Examples: One Dimension Nonlinear Equations Solutions and Sensitivity Convergence Nonlinear equations can have any number of solutions exp(x) + 1 = 0 has no solution exp( x) x = 0 has one solution x 2 4 sin(x) = 0 has two solutions x 3 + 6x x 6 = 0 has three solutions sin(x) = 0 has infinitely many solutions Michael T. Heath Scientific Computing 7 / 55

8 Nonlinear Equations Solutions and Sensitivity Convergence Example: Systems in Two Dimensions x 2 1 x 2 + γ = 0 x 1 + x γ = 0 Michael T. Heath Scientific Computing 8 / 55

9 Multiplicity Nonlinear Equations Nonlinear Equations Solutions and Sensitivity Convergence If f(x ) = f (x ) = f (x ) = = f (m 1) (x ) = 0 but f (m) (x ) 0 (i.e., mth derivative is lowest derivative of f that does not vanish at x ), then root x has multiplicity m If m = 1 (f(x ) = 0 and f (x ) 0), then x is simple root Michael T. Heath Scientific Computing 9 / 55

10 Sensitivity and Conditioning Nonlinear Equations Solutions and Sensitivity Convergence Conditioning of root finding problem is opposite to that for evaluating function Absolute condition number of root finding problem for root x of f : R R is 1/ f (x ) Root is ill-conditioned if tangent line is nearly horizontal In particular, multiple root (m > 1) is ill-conditioned Absolute condition number of root finding problem for root x of f : R n R n is J 1 f (x ), where J f is Jacobian matrix of f, {J f (x)} ij = f i (x)/ x j Root is ill-conditioned if Jacobian matrix is nearly singular Michael T. Heath Scientific Computing 10 / 55

11 Sensitivity and Conditioning Nonlinear Equations Solutions and Sensitivity Convergence Michael T. Heath Scientific Computing 11 / 55

12 Sensitivity and Conditioning Nonlinear Equations Solutions and Sensitivity Convergence What do we mean by approximate solution ˆx to nonlinear system, f(ˆx) 0 or ˆx x 0? First corresponds to small residual, second measures closeness to (usually unknown) true solution x Solution criteria are not necessarily small simultaneously Small residual implies accurate solution only if problem is well-conditioned Michael T. Heath Scientific Computing 12 / 55

13 Convergence Rate Nonlinear Equations Solutions and Sensitivity Convergence For general iterative methods, define error at iteration k by e k = x k x where x k is approximate solution and x is true solution For methods that maintain interval known to contain solution, rather than specific approximate value for solution, take error to be length of interval containing solution Sequence converges with rate r if e k+1 lim k e k r = C for some finite nonzero constant C Michael T. Heath Scientific Computing 13 / 55

14 Convergence Rate, continued Nonlinear Equations Solutions and Sensitivity Convergence Some particular cases of interest r = 1: linear (C < 1) r > 1: superlinear r = 2: quadratic Convergence rate linear superlinear quadratic Digits gained per iteration constant increasing double Michael T. Heath Scientific Computing 14 / 55

15 Interval Bisection Method Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Bisection method begins with initial bracket and repeatedly halves its length until solution has been isolated as accurately as desired while ((b a) > tol) do m = a + (b a)/2 if sign(f(a)) = sign(f(m)) then a = m else b = m end end < interactive example > Michael T. Heath Scientific Computing 15 / 55

16 Example: Bisection Method f(x) = x 2 4 sin(x) = 0 Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods a f(a) b f(b) Michael T. Heath Scientific Computing / 55

17 Bisection Method, continued Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Bisection method makes no use of magnitudes of function values, only their signs Bisection is certain to converge, but does so slowly At each iteration, length of interval containing solution reduced by half, convergence rate is linear, with r = 1 and C = 0.5 One bit of accuracy is gained in approximate solution for each iteration of bisection Given starting interval [a, b], length of interval after k iterations is (b a)/2 k, so achieving error tolerance of tol requires ( ) b a log 2 tol iterations, regardless of function f involved Michael T. Heath Scientific Computing 17 / 55

18 Newton s Method Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Truncated Taylor series f(x + h) f(x) + f (x)h is linear function of h approximating f near x Replace nonlinear function f by this linear function, whose zero is h = f(x)/f (x) Zeros of original function and linear approximation are not identical, so repeat process, giving Newton s method x k+1 = x k f(x k) f (x k ) Michael T. Heath Scientific Computing 24 / 55

19 Newton s Method, continued Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Newton s method approximates nonlinear function f near x k by tangent line at f(x k ) Michael T. Heath Scientific Computing 25 / 55

20 Example: Newton s Method Use Newton s method to find root of Derivative is so iteration scheme is Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods f(x) = x 2 4 sin(x) = 0 f (x) = 2x 4 cos(x) x k+1 = x k x2 k 4 sin(x k) 2x k 4 cos(x k ) Taking x 0 = 3 as starting value, we obtain x f(x) f (x) h Michael T. Heath Scientific Computing 26 / 55

21 Convergence of Newton s Method Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Newton s method transforms nonlinear equation f(x) = 0 into fixed-point problem x = g(x), where and hence g(x) = x f(x)/f (x) g (x) = f(x)f (x)/(f (x)) 2 If x is simple root (i.e., f(x ) = 0 and f (x ) 0), then g (x ) = 0 Convergence rate of Newton s method for simple root is therefore quadratic (r = 2) But iterations must start close enough to root to converge < interactive example > Michael T. Heath Scientific Computing 27 / 55

22 Newton s Method, continued Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods For multiple root, convergence rate of Newton s method is only linear, with constant C = 1 (1/m), where m is multiplicity k f(x) = x 2 1 f(x) = x 2 2x Michael T. Heath Scientific Computing 28 / 55

23 Secant Method Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods For each iteration, Newton s method requires evaluation of both function and its derivative, which may be inconvenient or expensive In secant method, derivative is approximated by finite difference using two successive iterates, so iteration becomes x k x k 1 x k+1 = x k f(x k ) f(x k ) f(x k 1 ) Convergence rate of secant method is normally superlinear, with r Michael T. Heath Scientific Computing 29 / 55

24 Secant Method, continued Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Secant method approximates nonlinear function f by secant line through previous two iterates < interactive example > Michael T. Heath Scientific Computing 30 / 55

25 Example: Secant Method Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Use secant method to find root of f(x) = x 2 4 sin(x) = 0 Taking x 0 = 1 and x 1 = 3 as starting guesses, we obtain x f(x) h Michael T. Heath Scientific Computing 31 / 55

26 Higher-Degree Interpolation Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Secant method uses linear interpolation to approximate function whose zero is sought Higher convergence rate can be obtained by using higher-degree polynomial interpolation For example, quadratic interpolation (Muller s method) has superlinear convergence rate with r Unfortunately, using higher degree polynomial also has disadvantages interpolating polynomial may not have real roots roots may not be easy to compute choice of root to use as next iterate may not be obvious Michael T. Heath Scientific Computing 32 / 55

27 Safeguarded Methods Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Rapidly convergent methods for solving nonlinear equations may not converge unless started close to solution, but safe methods are slow Hybrid methods combine features of both types of methods to achieve both speed and reliability Use rapidly convergent method, but maintain bracket around solution If next approximate solution given by fast method falls outside bracketing interval, perform one iteration of safe method, such as bisection Michael T. Heath Scientific Computing 38 / 55

28 Safeguarded Methods, continued Bisection Method Fixed-Point Iteration and Newton s Method Additional Methods Fast method can then be tried again on smaller interval with greater chance of success Ultimately, convergence rate of fast method should prevail Hybrid approach seldom does worse than safe method, and usually does much better Michael T. Heath Scientific Computing 39 / 55

29 Systems of Nonlinear Equations Fixed-Point Iteration Newton s Method Secant Updating Methods Solving systems of nonlinear equations is much more difficult than scalar case because Wider variety of behavior is possible, so determining existence and number of solutions or good starting guess is much more complex There is no simple way, in general, to guarantee convergence to desired solution or to bracket solution to produce absolutely safe method Computational overhead increases rapidly with dimension of problem Michael T. Heath Scientific Computing 41 / 55

30 Newton s Method Fixed-Point Iteration Newton s Method Secant Updating Methods In n dimensions, Newton s method has form x k+1 = x k J(x k ) 1 f(x k ) where J(x) is Jacobian matrix of f, {J(x)} ij = f i(x) x j In practice, we do not explicitly invert J(x k ), but instead solve linear system J(x k )s k = f(x k ) for Newton step s k, then take as next iterate x k+1 = x k + s k Michael T. Heath Scientific Computing 43 / 55

31 Example: Newton s Method Fixed-Point Iteration Newton s Method Secant Updating Methods Use Newton s method to solve nonlinear system [ ] x1 + 2x f(x) = 2 2 x x2 2 4 = 0 [ ] 1 2 Jacobian matrix is J f (x) = 2x 1 8x 2 If we take x 0 = [ 1 2 ] T, then [ ] [ ] f(x 0 ) =, J 13 f (x 0 ) = 2 16 [ ] [ ] Solving system s = gives s 13 0 = so x 1 = x 0 + s 0 = [ ] T [ ] 1.83, 0.58 Michael T. Heath Scientific Computing 44 / 55

32 Example, continued Fixed-Point Iteration Newton s Method Secant Updating Methods Evaluating at new point, [ ] [ ] f(x 1 ) =, J 4.72 f (x 1 ) = [ ] [ ] Solving system s = gives 4.72 s 1 = [ ] T, so x2 = x 1 + s 1 = [ ] T Evaluating at new point, [ ] 0 f(x 2 ) =, J 0.83 f (x 2 ) = [ ] Iterations eventually convergence to solution x = [ 0 1 ] T < interactive example > Michael T. Heath Scientific Computing 45 / 55

33 Fixed-Point Iteration Newton s Method Secant Updating Methods Convergence of Newton s Method Differentiating corresponding fixed-point operator g(x) = x J(x) 1 f(x) and evaluating at solution x gives G(x ) = I (J(x ) 1 J(x ) + n f i (x )H i (x )) = O where H i (x) is component matrix of derivative of J(x) 1 Convergence rate of Newton s method for nonlinear systems is normally quadratic, provided Jacobian matrix J(x ) is nonsingular But it must be started close enough to solution to converge i=1 Michael T. Heath Scientific Computing 46 / 55

34 Cost of Newton s Method Fixed-Point Iteration Newton s Method Secant Updating Methods Cost per iteration of Newton s method for dense problem in n dimensions is substantial Computing Jacobian matrix costs n 2 scalar function evaluations Solving linear system costs O(n 3 ) operations Michael T. Heath Scientific Computing 47 / 55

35 Secant Updating Methods Fixed-Point Iteration Newton s Method Secant Updating Methods Secant updating methods reduce cost by Using function values at successive iterates to build approximate Jacobian and avoiding explicit evaluation of derivatives Updating factorization of approximate Jacobian rather than refactoring it each iteration Most secant updating methods have superlinear but not quadratic convergence rate Secant updating methods often cost less overall than Newton s method because of lower cost per iteration Michael T. Heath Scientific Computing 48 / 55

36 Broyden s Method Fixed-Point Iteration Newton s Method Secant Updating Methods Broyden s method is typical secant updating method Beginning with initial guess x 0 for solution and initial approximate Jacobian B 0, following steps are repeated until convergence x 0 = initial guess B 0 = initial Jacobian approximation for k = 0, 1, 2,... Solve B k s k = f(x k ) for s k x k+1 = x k + s k y k = f(x k+1 ) f(x k ) B k+1 = B k + ((y k B k s k )s T k )/(st k s k) end Michael T. Heath Scientific Computing 49 / 55

37 Broyden s Method, continued Fixed-Point Iteration Newton s Method Secant Updating Methods Motivation for formula for B k+1 is to make least change to B k subject to satisfying secant equation B k+1 (x k+1 x k ) = f(x k+1 ) f(x k ) In practice, factorization of B k is updated instead of updating B k directly, so total cost per iteration is only O(n 2 ) Michael T. Heath Scientific Computing 50 / 55

38 Example: Broyden s Method Fixed-Point Iteration Newton s Method Secant Updating Methods Use Broyden s method to solve nonlinear system [ ] x1 + 2x f(x) = 2 2 x x2 2 4 = 0 If x 0 = [ 1 2 ] T, then f(x0 ) = [ 3 13 ] T, and we choose [ ] 1 2 B 0 = J f (x 0 ) = 2 16 Solving system gives s 0 = [ ] 1 2 s = [ ] 3 13 [ ] 1.83, so x = x 0 + s 0 = [ ] Michael T. Heath Scientific Computing 51 / 55

39 Example, continued Fixed-Point Iteration Newton s Method Secant Updating Methods Evaluating at new point x 1 gives f(x 1 ) = [ ] 3 y 0 = f(x 1 ) f(x 0 ) = 8.28 From updating formula, we obtain [ ] [ ] B 1 = = [ ] 0, so 4.72 [ ] Solving system [ ] [ ] s = 4.72 [ ] [ ] gives s 1 =, so x = x 1 + s 1 = Michael T. Heath Scientific Computing 52 / 55

40 Example, continued Fixed-Point Iteration Newton s Method Secant Updating Methods Evaluating at new point x 2 gives f(x 2 ) = [ ] 0 y 1 = f(x 2 ) f(x 1 ) = 3.64 [ ] 0, so 1.08 From updating formula, we obtain [ ] [ ] [ ] B 2 = + = Iterations continue until convergence to solution x = < interactive example > [ ] 0 1 Michael T. Heath Scientific Computing 53 / 55

41 Robust Newton-Like Methods Fixed-Point Iteration Newton s Method Secant Updating Methods Newton s method and its variants may fail to converge when started far from solution Safeguards can enlarge region of convergence of Newton-like methods Simplest precaution is damped Newton method, in which new iterate is x k+1 = x k + α k s k where s k is Newton (or Newton-like) step and α k is scalar parameter chosen to ensure progress toward solution Parameter α k reduces Newton step when it is too large, but α k = 1 suffices near solution and still yields fast asymptotic convergence rate Michael T. Heath Scientific Computing 54 / 55

42 Trust-Region Methods Fixed-Point Iteration Newton s Method Secant Updating Methods Another approach is to maintain estimate of trust region where Taylor series approximation, upon which Newton s method is based, is sufficiently accurate for resulting computed step to be reliable Adjusting size of trust region to constrain step size when necessary usually enables progress toward solution even starting far away, yet still permits rapid converge once near solution Unlike damped Newton method, trust region method may modify direction as well as length of Newton step Michael T. Heath Scientific Computing 55 / 55