Explore: Approximation By Arbitrary Linear Expressions

to approximation of functions is the asymptotic value of a function, i.e. the value as one or more of a function's parameters becomes arbitrarily large

+a_{n}(x)y^{(n)}=b(x)} where a0(x), ..., an(x) and b(x) are arbitrary differentiable functions that do not need to be linear, and y′, ..., y(n) are the successive derivatives

example, in polynomial regression, which uses linear regression to fit the response variable as an arbitrary polynomial function (up to a given degree) of

The approximation error in a given data value represents the significant discrepancy that arises when an exact, true value is compared against some approximation

systems, which cannot be modeled with linear algebra, it is often used for dealing with first-order approximations, using the fact that the differential

approximation formula is a rational expression in one variable for the computation of the approximate values of the trigonometric sines discovered by

In computer science, arbitrary-precision arithmetic, also called bignum arithmetic, multiple-precision arithmetic, or sometimes infinite-precision arithmetic

In linear algebra, the trace of a square matrix A, denoted tr(A), is the sum of the elements on its main diagonal, a 11 + a 22 + ⋯ + a n n {\displaystyle

is known as the shift vector. At each iteration the model is linearized by approximation to a first-order Taylor polynomial expansion about β k {\displaystyle

quantum chemistry and molecular physics, the Born–Oppenheimer (BO) approximation is the assumption that the wave functions of atomic nuclei and electrons