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Linear approximation

In mathematics, a linear approximation is an approximation of a general function using a linear function (more precisely, an affine function). They are widely used in the method of finite differences to produce first order methods for solving or approximating solutions to equations. In mathematics, a linear approximation is an approximation of a general function using a linear function (more precisely, an affine function). They are widely used in the method of finite differences to produce first order methods for solving or approximating solutions to equations. Given a twice continuously differentiable function f {displaystyle f} of one real variable, Taylor's theorem for the case n = 1 {displaystyle n=1} states that where R 2 {displaystyle R_{2}} is the remainder term. The linear approximation is obtained by dropping the remainder: This is a good approximation for x {displaystyle x} when it is close enough to a {displaystyle a} ; since a curve, when closely observed, will begin to resemble a straight line. Therefore, the expression on the right-hand side is just the equation for the tangent line to the graph of f {displaystyle f} at ( a , f ( a ) ) {displaystyle (a,f(a))} . For this reason, this process is also called the tangent line approximation. If f {displaystyle f} is concave down in the interval between x {displaystyle x} and a {displaystyle a} , the approximation will be an overestimate (since the derivative is decreasing in that interval). If f {displaystyle f} is concave up, the approximation will be an underestimate. Linear approximations for vector functions of a vector variable are obtained in the same way, with the derivative at a point replaced by the Jacobian matrix. For example, given a differentiable function f ( x , y ) {displaystyle f(x,y)} with real values, one can approximate f ( x , y ) {displaystyle f(x,y)} for ( x , y ) {displaystyle (x,y)} close to ( a , b ) {displaystyle (a,b)} by the formula The right-hand side is the equation of the plane tangent to the graph of z = f ( x , y ) {displaystyle z=f(x,y)} at ( a , b ) . {displaystyle (a,b).} In the more general case of Banach spaces, one has where D f ( a ) {displaystyle Df(a)} is the Fréchet derivative of f {displaystyle f} at a {displaystyle a} .

[ "Nonlinear system", "Small-angle approximation", "Discrete dipole approximation codes", "Korringa–Kohn–Rostoker approximation", "Muffin-tin approximation" ]
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