260 likes | 524 Views
Numerical Analysis – Interpolation. Hanyang University Jong-Il Park. Fitting. Exact fit Interpolation Extrapolation Approximate fit. Extrapolation. x. Interpolation. x. x. x. x. Weierstrass Approximation Theorem. Approximation error. Better approximation.
E N D
Numerical Analysis –Interpolation Hanyang University Jong-Il Park
Fitting • Exact fit • Interpolation • Extrapolation • Approximate fit Extrapolation x Interpolation x x x x
Approximation error Better approximation
Illustration of Lagrange polynomial • Unique • Too much complex
Differences • Difference • Forward difference : • Backward difference : • Central difference : f
Divided Differences ; 1st order divided difference ; 2nd order divided difference
Newton’s Forward Difference Interpolating Polynomials(I) • Equal Interval h • Derivation n=1 n=2
Newton’s Forward Difference Interpolating Polynomials(II) Generalization • Error Analysis Binomial coef.
1 1 Intpl. of Multivariate Function • Successive univariate polynomial • Direct mutivariate polynomial 2 direct multivariate Successive univariate
Inverse Interpolation = finding x(f) • Utilization of Newton’s polynomial Solve for x 1st approximation 2nd approximation Repeat until a convergence
spline polynomial Spline Interpolation • Why spline? Linear spline Quadratic spline Cubic spline Continuity • Good approximation !! • Moderate complexity !!
Cubic spline interpolation(I) • Cubic Spline Interpolation at an interval 4 unknowns for each interval 4n unknowns for n intervals Conditions 1) 2) 3) continuity of f’ 4) continuity of f’’ n n n-1 n-1
Cubic spline interpolation(II) • Determining boundary condition Method 1 : Method 2 : Method 3 :
Eg. CG modeling Non-Uniform Rational B-Spline