Orlicz space

These spaces are called Orlicz spaces because Władysław Orlicz was the first who introduced them, in 1932. Some mathematicians, including Wojbor Woyczyński, Edwin Hewitt and Vladimir Mazya, include the name of Zygmunt Birnbaum as well, referring to his earlier joint work with Władysław Orlicz. However in the Birnbaum–Orlicz paper the Orlicz space is not introduced, neither explicitly nor implicitly, hence the name Orlicz space is preferred. By the same reasons this convention has been also openly criticized by another mathematician (and an expert in the history of Orlicz spaces), Lech Maligranda. Orlicz was confirmed as the person who introduced Orlicz spaces already by Stefan Banach in his 1932 monograph. ==Definition==

Let \mu be a σ-finite measure on a set X, and \Phi: [0, \infty) \to [0, \infty] a Young function; i.e., a convex, lower semicontinuous, and non-trivial function. Non-trivial in the sense that it is neither the zero function x \mapsto 0 nor the convex dual of the zero function :x \mapsto \begin{cases} \,\,\,0 & \text{ if } x = 0, \\ +\infty & \text{ otherwise.}\end{cases} Now let L^\dagger_\Phi be the set of measurable functions f:X\to\R such that the integral :\int_X \Phi(|f|)\, d\mu is finite, where, as usual, functions that agree almost everywhere are identified. This is not necessarily a vector space (for example, it might fail to be closed under scalar multiplication). The Orlicz space, denoted L_\Phi, is the vector space of functions spanned by L^\dagger_\Phi; that is, the smallest linear space containing L^\dagger_\Phi. Formally, :L_{\Phi} = \left\{f \;\bigg\vert \int_X \Phi(k|f|)\, d\mu 0\right\}. There is another Orlicz space, the small Orlicz space, defined by :M_{\Phi} = \left\{f \;\bigg\vert \int_X \Phi(k|f|)\, d\mu 0 \right\}. In other words, it is the largest linear space contained in L^\dagger_\Phi. Norm To define a norm on L_\Phi, let \Psi be the complementary Young function to \Phi; i.e., :\Psi(x) = \int_0^x (\Phi')^{-1}(t)\, dt. Note that Young's inequality for products holds: :ab\le \Phi(a) + \Psi(b). The norm is then given by :\|f\|_\Phi = \sup\left\{\|fg\|_1 \,\bigg\vert \int \Psi(|g|)\, d\mu \le 1\right\}. Furthermore, the space L_\Phi is precisely the space of measurable functions for which this norm is finite. An equivalent norm, called the Luxemburg norm, is defined on L_\Phi by :\|f\|'_\Phi = \inf\left\{k\in (0,\infty)\,\bigg\vert\int_X \Phi(|f|/k)\,d\mu\le 1 \right\}, and likewise L_\Phi(\mu) is the space of all measurable functions for which this norm is finite. The two norms are equivalent in the sense that \| f \|_{\Phi}' \leq \| f \|_{\Phi} \leq 2 \| f \|_{\Phi}' for all measurable f. Note that by the monotone convergence theorem, if 0 , then :\int_X \Phi(|f|/\|f\|_{\Phi}')\,d\mu\le 1 . == Examples ==

For any p \in [1, \infty], L^p space is an Orlicz space with Orlicz function \Phi (t) = t^p. Here :t^\infty = \begin{cases} 0 &\text{ if } t \in [0, 1], \\ +\infty &\text{ else.} \end{cases} When 1 , the small and the large Orlicz spaces for \Phi(t) = t^p are equal: M_{\Phi} \simeq L_{\Phi}. For an example where L^\dagger_\Phi is not a vector space, and is strictly smaller than L_\Phi, let X be the open unit interval (0,1), \Phi(t)=e^t-1-t, and f(t) = \log(t). Then af is in the space L_\Phi for all a\in\R but is only in L^\dagger_\Phi if |a|. ==Properties==

Proposition. The Orlicz norm is a norm. Proof. Since \Phi(x) > 0 for some x > 0, we have \|f \|_{\Phi} = 0 \to f = 0 a.e.. That \|kf\|_{\Phi} = |k| \|f\|_{\Phi} is obvious by definition. For triangular inequality, we have:\begin{aligned} & \int_{\mathcal{X}} \Phi\left(\frac{f+g}{\|f\|_\Phi+\|g\|_\Phi}\right) d \mu \\ = & \int_{\mathcal{X}} \Phi\left(\frac{\|f\|_\Phi}{\|f\|_\Phi+\|g\|_\Phi} \frac{f}{\|f\|_\Phi}+\frac{\|g\|_\Phi}{\|f\|_\Phi+\|g\|_\Phi} \frac{g}{\|g\|_\Phi}\right) d \mu \\ \leq & \frac{\|f\|_\Phi}{\|f\|_\Phi+\|g\|_\Phi} \int_{\mathcal{X}} \Phi\left(\frac{f}{\|f\|_\Phi}\right) d \mu+\frac{\|g\|_\Phi}{\|f\|_\Phi+\|g\|_\Phi} \int_{\mathcal{X}} \Phi\left(\frac{g}{\|g\|_\Phi}\right) d \mu \\ \leq & 1 \end{aligned}Theorem. The Orlicz space L^\varphi (X) is a Banach space — a complete normed vector space. Theorem. M_\Phi, L_{\Phi^*} are topological dual Banach spaces. In particular, if M_{\Phi} = L_{\Phi}, then L_{\Phi^*}, L_{\Phi} are topological dual spaces. In particular, L^p, L^q are dual Banach spaces when 1/p + 1/q = 1 and 1 . ==Relations to Sobolev spaces==

Certain Sobolev spaces are embedded in Orlicz spaces: for n>1 and X \subseteq \mathbb{R}^{n} open and bounded with Lipschitz boundary \partial X, we have :W_0^{1, n} (X) \subseteq L^\varphi (X) for :\varphi (t) := \exp \left( | t |^{n / (n - 1)} \right) - 1. This is the analytical content of the Trudinger inequality: For X \subseteq \mathbb{R}^{n} open and bounded with Lipschitz boundary \partial X, consider the space W_0^{k, p} (X) with k p = n and p > 1. Then there exist constants C_1, C_2 > 0 such that : \int_X \exp \left( \left( \frac{C_1 \| \mathrm{D}^k u \|_{L^p (X)}} \right)^{n / (n - k)} \right) \, \mathrm{d} x \leq C_2 | X |. ==Orlicz norm of a random variable==

Similarly, the Orlicz norm of a random variable characterizes it as follows: :\|X\|_\Psi \triangleq \inf\left\{k\in (0,\infty)\mid \operatorname{E}[ \Psi(|X|/k)] \le 1 \right\}. This norm is homogeneous and is defined only when this set is non-empty. When \Psi(x) = x^p, this coincides with the p-th moment of the random variable. Other special cases in the exponential family are taken with respect to the functions \Psi_q(x) = \exp(x^q)-1 (for q \geq 1 ). A random variable with finite \Psi_2 norm is said to be "sub-Gaussian" and a random variable with finite \Psi_1 norm is said to be "sub-exponential". Indeed, the boundedness of the \Psi_p norm characterizes the limiting behavior of the probability distribution function: :\|X\|_{\Psi_p} 0, so that the tail of the probability distribution function is bounded above by O(e^{-K' x^p}). The \Psi_1 norm may be easily computed from a strictly monotonic moment-generating function. For example, the moment-generating function of a chi-squared random variable X with K degrees of freedom is M_X(t) = (1-2t)^{-K/2}, so that the reciprocal of the \Psi_1 norm is related to the functional inverse of the moment-generating function: :\|X\|_{\Psi_1} ^{-1} = M_X^{-1}(2) = (1-4^{-1/K})/2. == References ==