**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Concept# Coefficient

Summary

In mathematics, a coefficient is a multiplicative factor involved in some term of a polynomial, a series, or an expression. It may be a number (dimensionless), in which case it is known as a numerical factor. It may also be a constant with units of measurement, in which it is known as a constant multiplier. In general, coefficients may be any expression (including variables such as a, b and c). When the combination of variables and constants is not necessarily involved in a product, it may be called a parameter.
For example, the polynomial 2x^2-x+3 has coefficients 2, −1, and 3, and the powers of the variable x in the polynomial ax^2+bx+c have coefficient parameters a, b, and c.
The , also known as constant term or simply constant is the quantity not attached to variables in an expression. For

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications

Loading

Related people

Loading

Related units

Loading

Related concepts

Loading

Related courses

Loading

Related lectures

Loading

Related concepts (41)

Mathematics

Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These top

Polynomial

In mathematics, a polynomial is an expression consisting of indeterminates (also called variables) and coefficients, that involves only the operations of addition, subtraction, multiplication, and po

Vector space

In mathematics and physics, a vector space (also called a linear space) is a set whose elements, often called vectors, may be added together and multiplied ("scaled") by numbers called scalars. Scal

Related people (7)

Related courses (65)

PHYS-216: Mathematical methods for physicists

This course complements the Analysis and Linear Algebra courses by providing further mathematical background and practice required for 3rd year physics courses, in particular electrodynamics and quantum mechanics.

COM-500: Statistical signal and data processing through applications

Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, with an application oriented approach and hands-on numerical exercises.

FIN-403: Econometrics

The course covers basic econometric models and methods that are routinely applied to obtain inference results in economic and financial applications.

Related publications (58)

Loading

Loading

Loading

Related units (5)

Let f be an integrable function on RN, a a point in RN and B a complex number. If the mean value of f on the sphere of centre a and radius r tends to B when r tends to 0, we show that the Fourier integral at a of f is summable to B in Cesàro means of order λ > (N-1)/2. Let now U be a bounded open subset of RN whose boundary ∂U is a real analytic submanifold of RN with dimension N-1. We deduce from the preceding result that the Fourier integral at a of the indicator function of U is summable in Cesàro means of order λ > (N-1)/2 to 1 if a ∈ U, to 1/2 if a ∈ ∂U and to 0 if a ∉ U. We then show that if the function defined on ∂U by y → ‖ y - a ‖ has only a finite number of critical points, then we can take λ less or equal to (N-1)/2 ; more precisely, it suffices to have λ > (N-3)/2 + σ(a|∂U), where σ (a|∂U) < 0 is the maximum of the oscillatory indices associated to the critical points of y → ‖ y - a ‖ ; this generalizes results obtained by Pinsky, Taylor and Popov in 1997. Finally, writing μ∂U for the natural measure supported by ∂U, P(D) for a differential operator with constant coefficients of order m and b for a C∞ function on RN, we show that, if a is a point outside ∂U such that ‖ y - a ‖ has only a finite number of critical points on ∂U, the Fourier integral at a of the distribution P(D) bμ∂U is summable to 0 in Cesàro means of order λ > (N-1)/2 + m + σ (a|∂U) ; this generalizes a result obtained by Gonzàlez Vieli in 2002.

Related lectures (133)

Let K be a field with char(K) ≠ 2. The Witt-Grothendieck ring (K) and the Witt ring W (K) of K are both quotients of the group ring ℤ[𝓖(K)], where 𝓖(K) := K*/(K*)2 is the square class group of K. Since ℤ[𝓖(K)] is integral, the same holds for (K) and W(K). The subject of this thesis is the study of annihilating polynomials for quadratic forms. More specifically, for a given quadratic form φ over K, we study polynomials P ∈ ℤ[X] such that P([φ]) = 0 or P({φ}) = 0. Here [φ] ∈ (K) denotes the isometry class and {φ} ∈ W(K) denotes the equivalence class of φ. The subset of ℤ[X] consisting of all annihilating polynomials for [φ], respectively {φ}, is an ideal, which we call the annihilating ideal of [φ], respectively {φ}. Chapter 1 is dedicated to the algebraic foundations for the study of annihilating polynomials for quadratic forms. First we study the general structure of ideals in ℤ[X], which later on allows us to efficiently determine complete sets of generators for annihilating ideals. Then we introduce a more natural setting for the study of annihilating polynomials for quadratic forms, i.e. we define Witt rings for groups of exponent 2. Both (K) and W(K) are Witt rings for the square class group 𝓖(K). Studying annihilating polynomials in this more general setting relieves us to a certain extent from having to distinguish between isometry and equivalence classes of quadratic forms. In Section 1.1 we study the structure of ideals in R[X], where R is a principal ideal domain. For an ideal I ⊂ R[X] there exist sets of generators, which can be obtained in a natural way by considering the leading coefficients of elements in I. These sets of generators are called convenient. By discarding super uous elements we obtain modest sets of generators, which under certain assumptions are minimal sets of generators for I. Let G be a group of exponent 2. In Section 1.2 we study annihilating polynomials for elements of ℤ[G]. With the help of the ring homomorphisms Hom(ℤ[G],ℤ) it is possible to completely classify annihilating polynomials for elements of ℤ[G]. Note that an annihilating polynomial for an element f ∈ ℤ[G] also annihilates the image of f in any quotient of ℤ[G]. In particular, Witt rings for G are quotients of ℤ[G]. In Section 1.3 we use the ring homomorphisms Hom(ℤ[G],ℤ) to describe the prime spectrum of ℤ[G]. The obtained results can then be employed for the characterisation of the prime spectrum of a Witt ring R for G. Section 1.4 is dedicated to proving the structure theorems for Witt rings. More precisely, we generalise the structure theorems for Witt rings of fields to the general setting of Witt rings for groups of exponent 2. Section 1.5 serves to summarise Chapter 1. If R is a Witt ring for G, then we use the structure theorems to determine, for an element x ∈ R, the specific shape of convenient and modest sets of generators for the annihilating ideal of x. In Chapter 2 we study annihilating polynomials for quadratic forms over fields. More specifically, we first consider fields K, over which quadratic forms can be classified with the help of the classical invariants. Calculations involving these invariants allow us to classify annihilating ideals for isometry and equivalence classes of quadratic forms over K. Then we apply methods from the theory of generic splitting to study annihilating polynomials for excellent quadratic forms. Throughout Chapter 2 we make heavy usage of the results obtained in Chapter 1. Let K be a field with char(K) ≠ 2. Section 2.1 constitutes an introduction to the algebraic theory of quadratic forms over fields. We introduce the Witt-Grothendieck ring (K) and the Witt ring W(K), and we show that these are indeed Witt rings for 𝓖(K). In addition we adapt the structure theorems to the specific setting of quadratic forms. In Section 2.2 we introduce Brauer groups and quaternion algebras, and in Section 2.3 we define the first three cohomological invariants of quadratic forms. In particular we use quaternion algebras to define the Clifford invariant. In Section 2.4 we begin our actual study of annihilating polynomials for quadratic forms. Henceforth it becomes necessary to distinguish between isometry and equivalence classes of quadratic forms. We start by classifying annihilating ideals for quadratic forms over fields K, for which (K) and W(K) have a particularly simple structure. Subsequently we use calculations involving the first three cohomological invariants to determine annihilating ideals for quadratic forms over a field K such that I3(K) = {0}, where I(K) ⊂ W(K) is the fundamental ideal. Local fields, which are a special class of such fields, are studied in Section 2.5. By applying the Hasse-Minkowski Theorem we can then determine annihilating ideals of quadratic forms over global fields. Section 2.6 serves as an introduction to the elementary theory of generic splitting. In particular we introduce Pfister neighbours and excellent quadratic forms, which are the subjects of study in Section 2.7. We use methods from generic splitting to study annihilating polynomials for Pfister neighbours. The obtained result can be applied inductively to obtain annihilating polynomials for excellent quadratic forms. We conclude the section by giving an alternative, elementary approach to the study of annihilating polynomials for excellent forms, which makes use of the fact that (K) and W(K) are quotients of ℤ[𝓖(K)].

Multiple generalized additive models are a class of statistical regression models wherein parameters of probability distributions incorporate information through additive smooth functions of predictors. The functions are represented by basis function expansions, whose coefficients are the regression parameters. The smoothness is induced by a quadratic roughness penalty on the functionsâ curvature, which is equivalent to a weighted $L_2$ regularization controlled by smoothing parameters. Regression fitting relies on maximum penalized likelihood estimation for the regression coefficients, and smoothness selection relies on maximum marginal likelihood estimation for the smoothing parameters.
Owing to their nonlinearity, flexibility and interpretability, generalized additive models are widely used in statistical modeling, but despite recent advances, reliable and fast methods for automatic smoothing in massive datasets are unavailable. Existing approaches are either reliable, complex and slow, or unreliable, simpler and fast, so a compromise must be made. A bridge between these categories is needed to extend use of multiple generalized additive models to settings beyond those possible in existing software. This thesis is one step in this direction. We adopt the marginal likelihood approach to develop approximate expectation-maximization methods for automatic smoothing, which avoid evaluation of expensive and unstable terms. This results in simpler algorithms that do not sacrifice reliability and achieve state-of-the-art accuracy and computational efficiency.
We extend the proposed approach to big-data settings and produce the first reliable, high-performance and distributed-memory algorithm for fitting massive multiple generalized additive models. Furthermore, we develop the underlying generic software libraries and make them accessible to the open-source community.