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diff --git a/book/module2/error.tex b/book/module2/error.tex deleted file mode 100644 index d515c4a..0000000 --- a/book/module2/error.tex +++ /dev/null @@ -1,97 +0,0 @@ -\section{Errors in Numerical -Computations}\label{errors-in-numerical-computations} - -In any numerical method, \textbf{error} is inevitable. Understanding -\textbf{what kinds of errors occur} and \textbf{why} is essential to -building reliable and accurate computations. - -We mainly classify errors into two major types: - Truncation Error - -Round-off Error - -\subsection{What is Error?}\label{what-is-error} - -Let's remind ourselves what error is: \[ -\text{Error} = \text{True Value} - \text{Approximate Value} -\] However, often the \textbf{true value} is unknown, so we focus on -\textbf{reducing} and \textbf{analyzing} different types of errors -instead of eliminating them completely. This can be done by using -relative error when using iterative methods and is calculated as -follows: \[ -\text{Relative Error} = \frac{\text{Best} - \text{Second to best}}{Best} -\] - -\subsection{Truncation Error}\label{truncation-error} - -Truncation error occurs \textbf{when an infinite process is approximated -by a finite process}.\\ -In simple terms, it happens \textbf{when you cut off or ``truncate'' -part of the computation}. An example of this could be using a finite -number of terms from a Taylor Series expansion to approximate a -function. - -Approximating $e^x$ by the first few terms of its Taylor series: - -$e^x \approx 1 + x +\frac{x^2}{2!} + \frac{x^3}{3!}$ - -The error comes from \textbf{neglecting} all the higher order terms -($\frac{x^4}{4!}, \frac{x^5}{5!}$), \ldots). - -Truncation error occurs when using numerical methods such as -approximating and calculating derivatives and integrals. A -representation of the truncation error is show in the figure below. -Using our numerical methods we are left if some degree of error. - -\begin{figure} -\centering -\includegraphics{figures/truncationError.png} -\caption{Representation of truncation error under a curve} -\end{figure} - -In order to reduce truncation error there are a few things we can do: - -Include more terms (higher-order methods) - Decrease step sizes (e.g., -smaller \(\Delta x\) in approximations) - Use better approximation -algorithms. - -\subsection{Round-off Error}\label{round-off-error} - -Round-off error is caused by \textbf{the limited precision} with which -computers represent numbers. Since computers cannot store an infinite -number of digits, \textbf{they round off} after a certain number of -decimal or binary places. For example, instead of representing pi with -infinite decimal places it may be rounded off to approximately 16 digits -depending on number of bits and the representation of the bits. - -In other words, round-off error happens because of how computers store -numbers. For a double-floating point, the number is stored using -64-bits. The more bits we use, the more precise of a number we can -store. However, it makes it costs us more memory making it more -computational expensive. - -While individual round-off errors may seem negligible, their effects can -\textbf{accumulate over repeated computations}, leading to significant -inaccuracies. This is particularly problematic in operations such as -\textbf{subtracting two nearly equal numbers}, where \textbf{loss of -significance} can occur, severely reducing numerical precision and -amplifying the impact of round-off error. - -\subsubsection{How to Reduce Round-off -Error:}\label{how-to-reduce-round-off-error} - -To reduce round-off error, use higher-precision data types when storing -numerical values. Additionally, code and algorithms should be structured -to \textbf{avoid subtracting nearly equal numbers}, a common source of -significant error. Finally, employing \textbf{numerically stable -algorithms} is essential for minimizing the accumulation of round-off -errors during computation. - -\subsection{Total Error}\label{total-error} - -Truncation and round-off error are inversely proportional, meaning that -if we decrease one, the other increases. If we want to minimize total -error we must find the optimal point between step size and error. - -\begin{figure} -\centering -\includegraphics{figures/totalError.png} -\caption{Total Error} -\end{figure} |
