15 - 15 A>>%This is an implementation of Runge-Kutta to...

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Unformatted text preview: 15.) A >> %This is an implementation of Runge-Kutta to approximate the solutions to question 15% >> h = .1; a = 1; b = 2; n = 10; y0 = 1; %These are the input values% >> t = a; w = 1; >> f = inline ('w/t - (w/t)^2') f = Inline function: f(t,w) = w/t - (w/t)^2 >> for i = 1:n K1 = h*f(t,w); K2 = h*f(t+h/2,w+(K1)/2); K3 = h*f(t+h/2,w+(K2)/2); K4 = h*f(t+h,w+K3); w = w + (K1+2*K2+2*K3+K4)/6; %Computes wi% t = a + i*h; %Computes ti% [t' w'] end ans = 1.1000 1.0043 ans = 1.2000 1.0150 ans = 1.3000 1.0298 ans = 1.4000 1.0475 ans = 1.5000 1.0673 ans = 1.6000 1.0884 ans = 1.7000 1.1107 ans = 1.8000 1.1337 ans = 1.9000 1.1572 ans = 2.0000 1.1812 Actual Solution = 2/(1+ln(2)) = 1.1812322183 15.) B >> %This is an implementation of Runge-Kutta to approximate the solutions to question 15% >> h = .2; a = 1; b = 3; n = 10; y0 = 0; %These are the input values% >> t = a; w = 0; >> f = inline ('1+w/t+(w/t)^2') f = Inline function: f(t,w) = 1+w/t+(w/t)^2 >> for i = 1:n K1 = h*f(t,w); K2 = h*f(t+h/2,w+(K1)/2);...
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This note was uploaded on 05/01/2009 for the course PSTAT 120A taught by Professor Mackgalloway during the Spring '08 term at UCSB.

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15 - 15 A>>%This is an implementation of Runge-Kutta to...

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