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Systems of Nonlinear Equations

Systems of Nonlinear Equations. Paul Heckbert Computer Science Department Carnegie Mellon University. Nonlinear Systems. One dimensional: f : R  R e.g. f(x) = ax-b = 0 (linear) or f(x) = sin(2 x )- x = 0 (nonlinear) Multidimensional f : R n  R n

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Systems of Nonlinear Equations

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  1. Systems of Nonlinear Equations Paul Heckbert Computer Science Department Carnegie Mellon University 15-859B - Introduction to Scientific Computing

  2. Nonlinear Systems One dimensional: f : R  R e.g. f(x) = ax-b = 0 (linear) or f(x) = sin(2x)-x = 0 (nonlinear) Multidimensional f : Rn Rn e.g. f(x) = Ax-b = 0 (linear system) or {x2+y2+xy-1=0, x2+y2-xy-1=0} (nonlinear: two ellipses) also known as root finding 15-859B - Introduction to Scientific Computing

  3. Four 1-D Root-Finding Methods • Bisection Method • Newton’s Method • Secant Method • Linear Fractional Interpolation 15-859B - Introduction to Scientific Computing

  4. Bisection Method (Binary Search) Given f(), tol, and [a,b] such that sign(f(a))  sign(f(b)): while b-a>tol m = (a+b)/2 midpoint if sign(f(a)) = sign(f(m)): then a = m recurse on right half else b = m recurse on left half Guaranteed convergence! (if you can find initial a,b) but only at a linear rate: |errork+1| c|errork|, c<1 15-859B - Introduction to Scientific Computing

  5. Newton’s Method Given f(),f’(), tol, and initial guess x0 k = 0 do xk+1 = xk - f(xk)/f’(xk) k++ while xk - xk-1 > tol Can diverge (especially if f’ 0). If it converges, does so at quadratic rate: |ek+1|  c|ek|2. Requires derivative computation. 15-859B - Introduction to Scientific Computing

  6. Secant Method Like Newton, but approximates slope using point pairs Given f(), tol, and initial guesses x0, x1 k = 1 do xk+1 = xk - f(xk)(xk - xk-1)/(f(xk)-f(xk-1)) k++ while xk - xk-1 < tol Can diverge. If it converges, does so at rate 1.618: |ek+1|  c|ek|1.618. 15-859B - Introduction to Scientific Computing

  7. Linear Fractional Interpolation Instead of linear approximation, fit with fractional linear approximation: f(x)(x-u)/(vx-w) for some u,v,w Given f(), tol, and initial guesses a, b, c, fa=f(a), fb=f(b) do h = (a-c)(b-c)(fa-fb)fc / [(a-c)(fc-fb)fa-(b-c)(fc-fa)fb] a = b; fa = fb b = c; fb = fc c += h; fc = f(c) while h > tol If it converges, does so at rate 1.839: |ek+1|  c|ek|1.839. 15-859B - Introduction to Scientific Computing

  8. 1-D Root-Finding Summary • Bisection: safe: never diverges, but slow. • Newton’s method: risky but fast! • Secant method & linear fractional interpolation : less risky, mid-speed. • Hybrids: use a safe method where function poorly behaved, Newton’s method where it’s well-behaved. 15-859B - Introduction to Scientific Computing

  9. Multidimensional Newton’s Method f and x are n-vectors First order Taylor series approx.: f(x+h)  f(x)+f’(x)h f(x+h)=0 implies h=-J-1f (don’t compute J-1, but solve Jh=f ) so iteration should be xk+1 = xk - J-1(xk)f(xk) Method requires calculation of Jacobian – can be expensive 15-859B - Introduction to Scientific Computing

  10. Example: Intersection of Ellipses 2 equations in 2 unknowns: x2+y2+xy-1=0 x2+y2-xy-1=0 15-859B - Introduction to Scientific Computing

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