Approximaatiot

Approximations are estimates of quantities that are difficult or impossible to compute exactly. They play a central role in mathematics, science, and engineering, where exact expressions may be unavailable or too costly to obtain. An approximation is typically accompanied by an error bound or a statement of how close it is to the truth.

There are analytical, numerical, and statistical kinds of approximations. Analytical approximations aim to replace a complex expression or model with a simpler closed-form that captures essential behavior. Numerical approximations replace a problem with a discretized or finite version that can be solved by computers. Statistical approximations use probabilistic methods to estimate quantities when data are noisy or incomplete.

Common methods include Taylor series and other expansions for function values near a point, asymptotic expansions for large or small parameters, polynomial and rational approximations, and interpolation. Rational approximations such as Padé approximants often converge faster than polynomials. Chebyshev and other minimax approaches seek to minimize the maximum error over an interval. In numerical analysis, truncation and rounding introduce errors that are analyzed and controlled.

Error concepts such as absolute error, relative error, and error bounds guide the assessment of an approximation. Convergence describes how the approximation improves as more terms are included or the discretization becomes finer, while stability concerns how errors propagate through computations.

Historically and theoretically, approximation theory studies what can be approximated and how well. Early results, including the Weierstrass approximation theorem, establish that many functions can be closely represented by simpler objects like polynomials. Practical approximations are essential across science, engineering, and data analysis.