diff options
Diffstat (limited to 'book/module2/error.tex')
| -rw-r--r-- | book/module2/error.tex | 97 |
1 files changed, 97 insertions, 0 deletions
diff --git a/book/module2/error.tex b/book/module2/error.tex new file mode 100644 index 0000000..2646dc8 --- /dev/null +++ b/book/module2/error.tex @@ -0,0 +1,97 @@ +\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 π 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} |
