Homogeneous function

In mathematics, a homogeneous function is a function of several variables such that the following holds: If each of the function's arguments is multiplied by the same scalar, then the function's value is multiplied by some power of this scalar; the power is called the degree of homogeneity, or simply the degree. That is, if is an integer, a function of variables is homogeneous of degree if : f(sx_1,\ldots, sx_n)=s^k f(x_1,\ldots, x_n) for every x_1, \ldots, x_n, and s\ne 0. This is also referred to a th-degree or th-order homogeneous function. For example, a homogeneous polynomial of degree defines a homogeneous function of degree . The above definition extends to functions whose domain and codomain are vector spaces over a field : a function f : V \to W between two -vector spaces is homogeneous of degree k if for all nonzero s \in F and v \in V. This definition is often further generalized to functions whose domain is not , but a cone in , that is, a subset of such that \mathbf{v}\in C implies s \mathbf{v}\in C for every nonzero scalar . In the case of functions of several real variables and real vector spaces, a slightly more general form of homogeneity called positive homogeneity is often considered, by requiring only that the above identities hold for s > 0, and allowing any real number as a degree of homogeneity. Every homogeneous real function is positively homogeneous.…

In mathematics, a homogeneous function is a function of several variables such that the following holds: If each of the function's arguments is multiplied by the same scalar, then the function's value is multiplied by some power of this scalar; the power is called the degree of homogeneity, or simply the degree. That is, if is an integer, a function of variables is homogeneous of degree if f(sx_1,\ldots, sx_n)=s^k f(x_1,\ldots, x_n) for every x_1, \ldots, x_n, and s\ne 0. This is also referred to a th-degree or th-order homogeneous function. For example, a homogeneous polynomial of degree defines a homogeneous function of degree . The above definition extends to functions whose domain and codomain are vector spaces over a field : a function f : V \to W between two -vector spaces is homogeneous of degree k if for all nonzero s \in F and v \in V. This definition is often further generalized to functions whose domain is not , but a cone in , that is, a subset of such that \mathbf{v}\in C implies s \mathbf{v}\in C for every nonzero scalar . In the case of functions of several real variables and real vector spaces, a slightly more general form of homogeneity called positive homogeneity is often considered, by requiring only that the above identities hold for s > 0, and allowing any real number as a degree of homogeneity. Every homogeneous real function is positively homogeneous. The converse is not true, but is locally true in the sense that (for integer degrees) the two kinds of homogeneity cannot be distinguished by considering the behavior of a function near a given point. A norm over a real vector space is an example of a positively homogeneous function that is not homogeneous. A special case is the absolute value of real numbers. The quotient of two homogeneous polynomials of the same degree gives an example of a homogeneous function of degree zero. This example is fundamental in the definition of projective schemes. f(sx) = s^kf(x) for some integer , every x\in C, and every nonzero s\in F. The integer is called the degree of homogeneity, or simply the degree of . A typical example of a homogeneous function of degree is the function defined by a homogeneous polynomial of degree . The rational function defined by the quotient of two homogeneous polynomials is a homogeneous function; its degree is the difference of the degrees of the numerator and the denominator; its cone of definition is the linear cone of the points where the value of denominator is not zero. Homogeneous functions play a fundamental role in projective geometry since any homogeneous function from to defines a well-defined function between the projectivizations of and . The homogeneous rational functions of degree zero (those defined by the quotient of two homogeneous polynomial of the same degree) play an essential role in the Proj construction of projective schemes. Even in the case of integer degrees, there are many useful functions that are positively homogeneous without being homogeneous. This is, in particular, the case of the absolute value function and norms, which are all positively homogeneous of degree . They are not homogeneous since |-x|=|x|\neq -|x| if x\neq 0. This remains true in the complex case, since the field of the complex numbers \C and every complex vector space can be considered as real vector spaces. Euler's homogeneous function theorem is a characterization of positively homogeneous differentiable functions, which may be considered as the fundamental theorem on homogeneous functions. ==Examples== , as shown by this example. This is the function f defined by f(x,y) = x if xy > 0 and f(x, y) = 0 if xy \leq 0. This function is homogeneous of degree 1, that is, f(s x, s y) = s f(x,y) for any real numbers s, x, y. It is discontinuous at y = 0, x \neq 0. ]] ===Simple example=== The function f(x, y) = x^2 + y^2 is homogeneous of degree 2: f(tx, ty) = (tx)^2 + (ty)^2 = t^2 \left(x^2 + y^2\right) = t^2 f(x, y). ===Absolute value and norms=== The absolute value of a real number is a positively homogeneous function of degree , which is not homogeneous, since |sx|=s|x| if s>0, and |sx|=-s|x| if s The absolute value of a complex number is a positively homogeneous function of degree 1 over the real numbers (that is, when considering the complex numbers as a vector space over the real numbers). It is not homogeneous, over the real numbers as well as over the complex numbers. More generally, every norm and seminorm is a positively homogeneous function of degree which is not a homogeneous function. As for the absolute value, if the norm or semi-norm is defined on a vector space over the complex numbers, this vector space has to be considered as vector space over the real number for applying the definition of a positively homogeneous function. ===Linear maps=== Any linear map f : V \to W between vector spaces over a field is homogeneous of degree 1, by the definition of linearity: f(\alpha \mathbf{v}) = \alpha f(\mathbf{v}) for all \alpha \in {F} and v \in V. Similarly, any multilinear function f : V_1 \times V_2 \times \cdots V_n \to W is homogeneous of degree n, by the definition of multilinearity: f\left(\alpha \mathbf{v}_1, \ldots, \alpha \mathbf{v}_n\right) = \alpha^n f(\mathbf{v}_1, \ldots, \mathbf{v}_n) for all \alpha \in {F} and v_1 \in V_1, v_2 \in V_2, \ldots, v_n \in V_n. ===Homogeneous polynomials=== Monomials in n variables define homogeneous functions f : \mathbb{F}^n \to \mathbb{F}. For example, f(x, y, z) = x^5 y^2 z^3 \, is homogeneous of degree 10 since f(\alpha x, \alpha y, \alpha z) = (\alpha x)^5(\alpha y)^2(\alpha z)^3 = \alpha^{10} x^5 y^2 z^3 = \alpha^{10} f(x, y, z). \, The degree is the sum of the exponents on the variables; in this example, 10 = 5 + 2 + 3. A homogeneous polynomial is a polynomial made up of a sum of monomials of the same degree. For example, x^5 + 2x^3 y^2 + 9xy^4 is a homogeneous polynomial of degree 5. Homogeneous polynomials also define homogeneous functions. Given a homogeneous polynomial of degree k with real coefficients that takes only positive values, one gets a positively homogeneous function of degree k/d by raising it to the power 1 / d. So for example, the following function is positively homogeneous of degree 1 but not homogeneous: \left(x^2 + y^2 + z^2\right)^\frac{1}{2}. ===Min/max=== For every set of weights w_1,\dots,w_n, the following functions are positively homogeneous of degree 1, but not homogeneous: \min\left(\frac{x_1}{w_1}, \dots, \frac{x_n}{w_n}\right) (Leontief utilities) \max\left(\frac{x_1}{w_1}, \dots, \frac{x_n}{w_n}\right) ===Rational functions=== Rational functions formed as the ratio of two polynomials are homogeneous functions in their domain, that is, off of the linear cone formed by the zeros of the denominator. Thus, if f is homogeneous of degree m and g is homogeneous of degree n, then f / g is homogeneous of degree m - n away from the zeros of g. ===Non-examples=== The homogeneous real functions of a single variable have the form x\mapsto cx^k for some constant . So, the affine function x\mapsto x+5, the natural logarithm x\mapsto \ln(x), and the exponential function x\mapsto e^x are not homogeneous. In the case of a function of a single real variable ( n = 1 ), the theorem implies that a continuously differentiable and positively homogeneous function of degree has the form f(x)=c_+ x^k for x>0 and f(x)=c_- x^k for x The constants c_+ and c_- are not necessarily the same, as it is the case for the absolute value. ==Application to differential equations== The substitution v = y / x converts the ordinary differential equation I(x, y)\frac{\mathrm{d}y}{\mathrm{d}x} + J(x,y) = 0, where I and J are homogeneous functions of the same degree, into the separable differential equation x \frac{\mathrm{d}v}{\mathrm{d}x} = - \frac{J(1,v)}{I(1,v)} - v. f(mx) = m^k f(x). If in addition there is a function M \to M, denoted by m \mapsto |m|, called an then f is said to be if for every x \in X and m \in M, f(mx) = |m|^k f(x). A function is (resp. ) if it is homogeneous of degree 1 over M (resp. absolutely homogeneous of degree 1 over M ). More generally, it is possible for the symbols m^k to be defined for m \in M with k being something other than an integer (for example, if M is the real numbers and k is a non-zero real number then m^k is defined even though k is not an integer). If this is the case then f will be called if the same equality holds: f(mx) = m^k f(x) \quad \text{ for every } x \in X \text{ and } m \in M. The notion of being is generalized similarly. ===Distributions (generalized functions)=== A continuous function f on \R^n is homogeneous of degree k if and only if \int_{\R^n} f(tx) \varphi(x)\, dx = t^k \int_{\R^n} f(x)\varphi(x)\, dx for all compactly supported test functions \varphi ; and nonzero real t. Equivalently, making a change of variable y = tx, f is homogeneous of degree k if and only if t^{-n}\int_{\R^n} f(y)\varphi\left(\frac{y}{t}\right)\, dy = t^k \int_{\R^n} f(y)\varphi(y)\, dy for all t and all test functions \varphi. The last display makes it possible to define homogeneity of distributions. A distribution S is homogeneous of degree k if t^{-n} \langle S, \varphi \circ \mu_t \rangle = t^k \langle S, \varphi \rangle for all nonzero real t and all test functions \varphi. Here the angle brackets denote the pairing between distributions and test functions, and \mu_t : \R^n \to \R^n is the mapping of scalar division by the real number t. ==Glossary of name variants== Let f : X \to Y be a map between two vector spaces over a field \mathbb{F} (usually the real numbers \R or complex numbers \Complex ). If S is a set of scalars, such as \Z, [0, \infty), or \Reals for example, then f is said to be if f(s x) = s f(x) for every x \in X and scalar s \in S. For instance, every additive map between vector spaces is S := \Q although it might not be S := \R. The following commonly encountered special cases and variations of this definition have their own terminology: ( ) : f(rx) = r f(x) for all x \in X and all real r > 0. When the function f is valued in a vector space or field, then this property is logically equivalent to , which by definition means: f(rx) = r f(x) for all x \in X and all real r \geq 0. It is for this reason that positive homogeneity is often also called nonnegative homogeneity. However, for functions valued in the extended real numbers [-\infty, \infty] = \Reals \cup \{\pm \infty\}, which appear in fields like convex analysis, the multiplication 0 \cdot f(x) will be undefined whenever f(x) = \pm \infty and so these statements are not necessarily always interchangeable. This property is used in the definition of a sublinear function. Minkowski functionals are exactly those non-negative extended real-valued functions with this property. : f(rx) = r f(x) for all x \in X and all real r. This property is used in the definition of a linear functional. : f(sx) = s f(x) for all x \in X and all scalars s \in \mathbb{F}. It is emphasized that this definition depends on the scalar field \mathbb{F} underlying the domain X. This property is used in the definition of linear functionals and linear maps. : f(sx) = \overline{s} f(x) for all x \in X and all scalars s \in \mathbb{F}. If \mathbb{F} = \Complex then \overline{s} typically denotes the complex conjugate of s . But more generally, as with semilinear maps for example, \overline{s} could be the image of s under some distinguished automorphism of \mathbb{F}. Along with additivity, this property is assumed in the definition of an antilinear map. It is also assumed that one of the two coordinates of a sesquilinear form has this property (such as the inner product of a Hilbert space). All of the above definitions can be generalized by replacing the condition f(rx) = r f(x) with f(rx) = |r| f(x), in which case that definition is prefixed with the word or For example, : f(sx) = |s| f(x) for all x \in X and all scalars s \in \mathbb{F}. This property is used in the definition of a seminorm and a norm. If k is a fixed real number then the above definitions can be further generalized by replacing the condition f(rx) = r f(x) with f(rx) = r^k f(x) (and similarly, by replacing f(rx) = |r| f(x) with f(rx) = |r|^k f(x) for conditions using the absolute value, etc.), in which case the homogeneity is said to be (where in particular, all of the above definitions are ). For instance, : f(rx) = r^k f(x) for all x \in X and all real r. : f(sx) = s^k f(x) for all x \in X and all scalars s \in \mathbb{F}. : f(rx) = |r|^k f(x) for all x \in X and all real r. : f(sx) = |s|^k f(x) for all x \in X and all scalars s \in \mathbb{F}. A nonzero continuous function that is homogeneous of degree k on \R^n \backslash \lbrace 0 \rbrace extends continuously to \R^n if and only if k > 0. ==References== ==Sources== ==External links==

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