Numerical Differentiation and Integration (4-1) Numerical Differentiation 1 (4-1) Numerical Differentiation MaSc 352 - PNU Forward and Backward Difference Formulas: 2 (4-1) Numerical Differentiation MaSc 352 - PNU 3 (4-1) Numerical Differentiation MaSc 352 - PNU Example (1) 4 (4-1) Numerical Differentiation MaSc 352 - PNU Obtaining General Derivative Approximation Formula: 108 5 (4-1) Numerical Differentiation MaSc 352 - PNU 6 (4-1) Numerical Differentiation MaSc 352 - PNU Three-Point Formula: Since From Eq. (4-3) we get: 7 (4-1) Numerical Differentiation MaSc 352 - PNU Three-Point Formula (When nodes are equally spaced): 8 (4-1) Numerical Differentiation MaSc 352 - PNU 9 (4-1) Numerical Differentiation MaSc 352 - PNU 10 (4-1) Numerical Differentiation MaSc 352 - PNU Five-Point Formula (nodes are equally spaced): 11 (4-1) Numerical Differentiation MaSc 352 - PNU Example (2) [Exercise 9 Page 177]: x f (x) 2.1 2.2 2.3 2.4 2.5 2.6 -1.7 -1.37 -1.12 -0.92 -0.75 -0.6 1 . f ′( 2 .1) 3-Point Endpoint 5-Point Endpoint 12 (4-1) Numerical Differentiation MaSc 352 - PNU 2 . f ′( 2 .3 ) 3-Point Endpoint 3-Point Midpoint 5-Point Midpoint 13 (4-1) Numerical Differentiation MaSc 352 - PNU Example (3) [Exercise 11 Page 177]: 1. Absolute error: f ′( 2 .2 ) 2. The error bound of using 3-Point Midpoint formula 14 (4-1) Numerical Differentiation MaSc 352 - PNU The error bound of using 3-Point Endpoint formula 15 (4-1) Numerical Differentiation MaSc 352 - PNU High Derivative Formula: Second Derivative Midpoint Formula: 16 (4-1) Numerical Differentiation MaSc 352 - PNU 17 (4-1) Numerical Differentiation MaSc 352 - PNU Example (4): Use The second derivative formula (4.9) to approximate f ′′( 2) using the following data: x f (x) 1.8 1.9 2.0 2.1 2.2 10.89 12.7 14.78 17.15 19.86 1. h= 2. h= 18 (4-1) Numerical Differentiation MaSc 352 - PNU Round-Off Error Instability: 19 (4-1) Numerical Differentiation MaSc 352 - PNU Example (5): Consider using the values in Table 4.3 to approximate f ′(0.9) where f ( x ) = sin x Compute the error and the optimal choice of h to reduce the error. We can notice that the best approximations are at: 20 (4-1) Numerical Differentiation MaSc 352 - PNU Optimal Choice of h: 21 (4-1) Numerical Differentiation MaSc 352 - PNU Homework: Exercise Set (4-1) 1 (b) +3 (b) 5 ( f ′(1.2) + f ′(1.4) ) +7 ( f ′(1.2) +f ′(1.4) ) 9 ( f ′( 2.4) + f ′(2.6) ) 20 22 (4-1) Numerical Differentiation MaSc 352 - PNU
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