Polynomials and Taylor’s Approximations
نویسندگان
چکیده
منابع مشابه
Orthogonal Polynomials and Polynomial Approximations
3.1.1. Existence and uniqueness. Our immediate goal is to establish the existence of a sequence of orthogonal polynomials. Although we could, in principle, determine the coefficients a j of pn in the natural basis by using the orthogonality conditions (3.1.2), it is computationally advantageous to express pn in terms of lower-order orthogonal polynomials. Let us denote Pn := span { 1, x, x, · ·...
متن کاملThe Taylors of Lancashire
JOHN L. WEST, The Taylors of Lancashire, Manchester, [the author], 1977, 8vo, pp. 134, illus., £1.50 + postage (paperback). (Obtainable from: 11 Half Edge Lane, Eccles, Manchester). An excellent account of a remarkable medical family, based on extensive research into manuscript and printed sources. It extended over six or seven generations, from James Taylor (1708/10-1777) of Whitworth to Herbe...
متن کاملMoment Approximations for Set-Semidefinite Polynomials
The set of polynomials which are nonnegative over a subset of the nonnegative orthant (we call them set semidefinite) have many uses in optimization. A common example of this type of set is the set of copositive matrices, where effectively we are considering nonnegativity over the entire nonnegative orthant and we limit the polynomials to be homogeneous of degree two. Lasserre in A new look at ...
متن کاملComputing Lower Rank Approximations of Matrix Polynomials
Given an input matrix polynomial whose coefficients are floating point numbers, we consider the problem of finding the nearest matrix polynomial which has rank at most a specified value. This generalizes the problem of finding a nearest matrix polynomial that is algebraically singular with a prescribed lower bound on the dimension given in a previous paper by the authors. In this paper we prove...
متن کاملConditional Density Approximations with Mixtures of Polynomials
Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn oneand multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Institute of Engineering
سال: 2017
ISSN: 1810-3383
DOI: 10.3126/jie.v12i1.16905