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80 Out[95]: 1.5707963267948966 |
80 Out[95]: 1.5707963267948966 |
81 |
81 |
82 In [96]: expression(pi/3) |
82 In [96]: expression(pi/3) |
83 Out[96]: 0.90689968211710881 |
83 Out[96]: 0.90689968211710881 |
84 \end{lstlisting} |
84 \end{lstlisting} |
85 \subsection{Roots of non-linear eqations} |
85 \subsection{Roots of non-linear equations} |
86 For Finding the roots of a non linear equation(defined as $f(x)=0$), around a starting estimate we use \typ{fsolve}:\\ |
86 For Finding the roots of a non linear equation(defined as $f(x)=0$), around a starting estimate we use \typ{fsolve}:\\ |
87 \typ{In []: from scipy.optimize import fsolve}\\ |
87 \typ{In []: from scipy.optimize import fsolve}\\ |
88 \typ{fsolve} is not a part of \typ{pylab}, instead is a function in the \textbf{optimize} module of \textbf{scipy}, and hence we \textbf{import} it.\\ |
88 \typ{fsolve} is not a part of \typ{pylab}, instead is a function in the \textbf{optimize} module of \textbf{scipy}, and hence we \textbf{import} it.\\ |
89 %\typ{fsolve} takes first argument as name of function, which evaluates $f(x)$, whose roots one wants to find. And second argument is starting estimate, around which roots are found. |
89 %\typ{fsolve} takes first argument as name of function, which evaluates $f(x)$, whose roots one wants to find. And second argument is starting estimate, around which roots are found. |
90 For illustration, we want to find roots of equation: |
90 For illustration, we want to find roots of equation: |