Let f(x) = xlnx. Approximate f(2) by the Hermite interpolating polynomial using x0 = 1 and x1 = e and compare the error.(e ≈ 2.7...)
firstly we obtained the value of f(2) by Hermite Interpolation and then the exact value and by comparing the two we found the approximate error.
Let f(x) = xlnx. Approximate f(2) by the Hermite interpolating polynomial using x0 = 1 and...
2. Consider interpolating the data (x0,yo), . . . , (x64%) given by Xi | 0.1 | 0.15 | 0.2 | 0.3 | 0.35 | 0.5 | 0.75 yi 4.0 1.0 1.22.12.02.52.5 For all tasks below, please submit your MATLAB code and your plots. You can write all code in a single (a) Using MATLAB, plot the interpolating (6th degree) polynomial given these data on the domain .m-file [0.1,0.75] using the polyfit and polyval commands. To learn how to use...
For an nth-order Newton's divided difference interpolating polynomial fn(x), the error of interpolation can be estimated by Rn-| g(xmPX, , xm» ,&J . (x-x-Xx-x.) . . . (x-x.) | , where (xo, f(xo)), (xi, fx)).., (Xn-1, f(xn-1) are data points; g[x-,x,,x-.., x,] is the (n+1)-th finite divided difference. To minimize Rn, if there are more than n+1 data points available for calculating fn(x) using the nth-order Newton's interpolating polynomial, n+1 data points (Xo, f(xo)), (x1, f(x)), , (in, f(%)) should...
Question 1 2 pts The Hermite Interpolation polynomial for 33 distinct nodes has Degree at most {Be Careful with the answer. Look at the Theorem and the Question Carefully; compare the given information} Question 2 2 pts If f € C4 [a, b] and p1, P2, P3, and p4 are Distinct Points in [a, b], Then 1. There are two 3rd divided differences 2. There is exactly one 3rd divided difference and it is equal to the value of f(iv)...
T47:02 731 VPN 97% 5 TOⓇ + : 4. Find the Hermite interpolating polynomial which interpolates the values f(1) = 4, f'(1) = -3, f(4) = 13, f'(4) = 9 and verify your answer. 5 13
Let f : [x0−h, x0+h]→R be defined. (a) Construct the 2nd degree Lagrange polynomial fitting {x −h,x ,x +h} and compute P′′(x ). (b) Use Taylor’s theorem to derive the same formula with error term.
this is numerical analysis 2. Consider the function f(x) = -21° +1. (a) Calculate the interpolating polynomial pz() for data using the nodes 2o = -1, 11 = 0, 12 = 1. Simplify the polynomial to standard form. Use the error theorem for polynomial interpolation to bound the error f(x) - P2(x) on the interval (-1,2). Is this bound realistic?
Let f(x) = do +212 + ... + and" be a polynomial of degree less than or equal to n, and let {x0, 21, ..., Un} be distinct points. What is the value of f (x0, X1, ... , Xn]? Explain.
Compute, using divided differences, the value of the piecewise cubic Her- mite interpolating polynomial at x = 11=10 given nodes at xi = i, for i = 1; : : : ; 10, and values and derivatives at the nodes from the function f(x) = 1=x. Remember iterative formula for divided differences: 2. (25 pts) Compute, using divided differences, the value of the piecewise cubic Her mite interpolating polynomial at x-11/10 given nodes at ai-i, for i-1, , 10. and...
5 Let f(3) = e', 0 <<< 2. Using the val e, 0 SXS 2. Using the values in the table below, perform the following computations x 0.0 0.5 1.0 2.0 f(x) 1.0 1.6487 2.7183 7.3890 (a) Approximate f(0.25) using linear interpolation with Xo = 0 and 21 = 0.5. (8 marks) (b) Approximate /(0.25) by using the quadratic interpolating polynomial with Xo = 0,2 = 1 and 2 = 2. [10 marks (c) Which approximations are better? Why? [2...
Consider polynomial interpolation of the function f(x)=1/(1+25x^2) on the interval [-1,1] by (1) an interpolating polynomial determined by m equidistant interpolation points, (2) an interpolating polynomial determined by interpolation at the m zeros of the Chebyshev polynomial T_m(x), and (3) by interpolating by cubic splines instead of by a polynomial. Estimate the approximation error by evaluation max_i |f(z_i)-p(z_i)| for many points z_i on [-1,1]. For instance, you could use 10m points z_i. The cubic spline interpolant can be determined in...