Backward Divided Difference Topic: Differentiation Major: General Engineering Authors: Autar Kaw, Sri Harsha Garapati 5/21/2008 http://numericalmethods.eng.usf.edu 1 Definition lim f ( x ) − f ( x − Δx ) f ′( x ) = Δx → 0 Δx . Slope at xi y f(x) xi 2 x http://numericalmethods.eng.usf.edu Backward Divided Difference f (x ) − f (x − Δx ) f ′( x ) ≅ Δx f (x) x − Δx 3 x f (x i ) − f (x i − Δ x ) f ′( x i ) ≅ Δx x http://numericalmethods.eng.usf.edu Example Example: The velocity of a rocket is given by ν (t ) = 2000 ⎡ ⎤ 14 × 10 4 ln ⎢ ⎥ − 9 . 8 t , 0 ≤ t ≤ 30 4 × − t 14 10 2100 ⎣ ⎦ where ν given in m/s and t is given in seconds. Use backward difference approximation Of the first derivative of ν(t ) Δt = 2s. Solution: a (ti ) ≅ to calculate the acceleration at t = 16s. Use a step size of ν (ti ) −ν (ti −1 ) Δt ti = 16 4 http://numericalmethods.eng.usf.edu Example (contd.) Δt = 2 ti −1 = ti − Δt = 16 − 2 = 14 a (16 ) = ν (16 ) −ν (14 ) 2 ⎡ ⎤ 14 ×10 4 ν (16) = 2000 ln ⎢ ⎥ − 9.8(16) = 392.07m / s 4 ( ) 14 × 10 − 2100 16 ⎣ ⎦ ⎡ ⎤ 14 ×10 4 = 334.24m / s ν (14) = 2000 ln ⎢ ⎥ − 9.8(14) 4 ⎣14 ×10 − 2100(14) ⎦ 5 http://numericalmethods.eng.usf.edu Example (contd.) Hence a(16) = ν (16) −ν (14) 2 The exact value of = 392.07 − 334.24 = 28.915m / s 2 a(16) can be calculated by differentiating ⎡ ⎤ 14 × 10 4 ν (t ) = 2000 ln ⎢ ⎥ − 9.8t 4 ⎣14 × 10 − 2100t ⎦ as d [ν (t )] = − 4040 − 29.4t dt − 200 + 3t a(16) = 29.674m / s 2 a(t ) = 6 http://numericalmethods.eng.usf.edu Example (contd.) The absolute relative true error is εt = TrueValue − ApproximateValue ×100 TrueValue 29.674 − 28.915 ×100 29.674 = 2.557% = 7 http://numericalmethods.eng.usf.edu Effect Of Step Size f ( x ) = 9e Value of h 0.05 0.025 0.0125 0.00625 0.003125 0.001563 0.000781 0.000391 0.000195 9.77E-05 4.88E-05 8 4x f ' (0.2) Using backward Divided difference method. f ' (0.2) 72.61598 76.24376 78.14946 79.12627 79.62081 79.86962 79.99442 80.05691 80.08818 80.10383 80.11165 Ea 3.627777 1.905697 0.976817 0.494533 0.248814 0.124796 0.062496 0.031272 0.015642 0.007823 εa % 4.758129 2.438529 1.234504 0.62111 0.311525 0.156006 0.078064 0.039047 0.019527 0.009765 Significant digits 1 1 1 1 2 2 2 3 3 3 Et εt % 7.50349 3.87571 1.97002 0.99320 0.49867 0.24985 0.12506 0.06256 0.03129 0.01565 0.00782 9.365377 4.837418 2.458849 1.239648 0.622404 0.31185 0.156087 0.078084 0.039052 0.019529 0.009765 http://numericalmethods.eng.usf.edu Effect of Step Size in Backward Divided Difference Method f'(0.2) 95 Initial step size=0.05 85 75 1 3 5 7 9 11 Num ber of tim es the step size is halved, n 9 http://numericalmethods.eng.usf.edu Effect of Step Size on Approximate Error Initial step size=0.05 4 Ea 3 2 1 0 1 3 5 7 9 11 Num ber of steps involved, n 10 http://numericalmethods.eng.usf.edu Effect of Step Size on Absolute Relative Approximate Error Initial step size=0.05 5 |Ea|,% 4 3 2 1 0 0 1 2 3 4 5 6 7 8 9 10 11 12 Num ber of steps involved, n 11 http://numericalmethods.eng.usf.edu Effect of Step Size on Least Number of Significant Digits Correct Least number of significant digits correct Initial step size=0.05 3.5 3 2.5 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 Num ber of steps involved, n 12 http://numericalmethods.eng.usf.edu Et Effect of Step Size on True Error 8 7 6 5 4 3 2 1 0 Initial step size=0.05 0 2 4 6 8 10 12 Num ber of steps involved, n 13 http://numericalmethods.eng.usf.edu Effect of Step Size on Absolute Relative True Error Initial step size=0.05 10 9 8 |Et| % 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 10 11 Num ber of steps involved, n 14 http://numericalmethods.eng.usf.edu