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Numerical methods are algorithms used to obtain approximate solutions for mathematical problems that are challenging to solve analytically. This chapter discusses the various types of errors that arise during numerical computations, the significance of floating-point representation, and the concepts of conditioning and stability in numerical algorithms. Understanding these foundational concepts is crucial for enhancing the reliability and accuracy of computational techniques in various scientific fields.
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ee4-nt-1.pdfClass Notes
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Term: Absolute Error
Definition: The difference between the exact value and approximate value.
Term: Relative Error
Definition: The absolute error normalized by the exact value.
Term: FloatingPoint Representation
Definition: Method for representing real numbers in a computer, using scientific notation.
Term: WellConditioned Problem
Definition: A problem where small changes to input produce small changes in output.
Term: IllConditioned Problem
Definition: A problem where small changes in input can cause large changes in output.
Term: Stability
Definition: The property of an algorithm to control the growth of numerical errors during computations.