Entropy is a measure of disorder, given by the amount of information necessary to precisely specify the state of a system.
Entropy is important in information theory and statistical physics.
We can give a precise mathematical definition of the entropy in probability theory.
We will want a couple of preliminary definitions. Fix a probability space $(X,\mu)$; that is, $X$ is a set, and $\mu$ is a probability measure on $X$.
If $A$ is a measurable subset of $X$, then the surprisal or self-information of $A$ (with respect to $\mu$) is
Notice that, despite the minus sign in this formula, $\sigma$ is a nonnegative function (since $\log p \leq 0$ for $p \leq 1$); more precisely, $\sigma$ takes values in $[0,infty]$. The term ‘surprisal’ is intended to suggest how surprised one ought to be upon learning that the event modelled by $A$ is true: from no surprise for an event with probability $1$ to infinite surprise for an event with probability $0$.
The expected surprisal of $A$ is then
(with $h_\mu(A) = 0$ when $\mu(A) = 0$). Like $\sigma$, $h$ is a nonnegative function; it is also important that $h_\mu$ is concave. Both $h_\mu(\nothing)$ and $h_\mu(X)$ are $0$, but for different reasons; $h_\mu(A) = 0$ when $\mu(A) = 1$ because, upon observing an event with probability $1$, one gains no information; while $h_\mu(A) = 0$ when $\mu(A) = 0$ because one expects never to observe an event with probability $0$. The maximum possible value of $h$ is $\mathrm{e}^{-1} \log \mathrm{e}$ (so $\mathrm{e}^{-1}$ if we use natural logarithms), which occurs when $\mu(A) = \mathrm{e}^{-1}$.
We have not specified the base of the logarithm, which amounts to a constant factor (proportional to the logarithm of the base), which we think of as specifying the unit of measurement of entropy. Common choices for the base are $2$ (whose unit is the bit, originally a unit of memory in computer science), $256$ (byte: $8$ bits), $3$ (trit), $\mathrm{e}$ (nat or neper), $10$ (bel, originally a unit of relative power intensity in telegraphy, or ban, dit, or hartley), and $\root{10}{10}$ (decibel: $1/10$ of a bel). In applications to statistical physics, common bases are approximately $10^{3.1456 \times 10^{22}}$ (joule per kelvin), $1.65404$ (calorie per mole-kelvin), etc.
Recall that a partition of a set $X$ is a family $\mathcal{P}$ of subsets of $X$ (the parts of the partition) such that $X$ is the union of the parts and any two distinct parts are disjoint (or better, for constructive mathematics, two parts are equal if their intersection is inhabited).
When $X$ is a probability space, we may relax both conditions: for the union of $\mathcal{P}$, we require only that it be a full set; for the intersections of pairs of elements of $\mathcal{P}$, we require only that they be null sets (or better, for constructive mathematics, that $A = B$ when $\mu^*(A \cap B) \gt 0$, where $\mu^*$ is the outer measure? corresponding to $\mu$).
For definiteness, call such a collection of subsets a $\mu$-almost partition; a $\mu$-almost partition is measurable if each of its part is measurable (in which case we can use $\mu$ instead of $\mu^*$).
This is a general mathematical definition of entropy.
Given a probability measure space $(X,\mu)$ and a $\sigma$-algebra $\mathcal{M}$ of measurable sets in $X$, the entropy of $\mathcal{M}$ with respect to $\mu$ is
In words, the entropy is the supremum, over all ways of expressing $X$ as an internal disjoint union of finitely many elements of the $\sigma$-algebra $\mathcal{M}$, of the sum, over these measurable sets, of the expected surprisals of these sets. This supremum can also be expressed as a limit as we take $\mathcal{F}$ to be finer and finer, since $h_\mu$ is concave and the partitions are directed.
We have written this so that $\mathcal{F}$ is a finite partition of $X$; without loss of generality, we may require only that $\mathcal{F}$ be a $\mu$-almost partition. In constructive mathematics, it seems that we must use this weakened condition, at least the part that allows $\bigcup \mathcal{F}$ to merely be full.
This definition is very general, and it is instructive to look at special cases.
Given a probability space $(X,\mu)$, the entropy of this probability space is the entropy, with respect to $\mu$, of the $\sigma$-algebra of all measurable subsets of $X$.
Every measurable almost-partition of a measure space (indeed, any family of measurable subsets) generates a $\sigma$-algebra. The entropy of a measurable almost-partition $\mathcal{P}$ of a probability measure space $(X,\mu)$ is the entropy, with respect to $\mu$, of the $\sigma$-algebra generated by $\mathcal{P}$. The formula (1) may then be written
since an infinite sum (of nonnegative terms) may also be defined as a supremum. (Actually, the supremum in the infinite sum does not quite match the supremum in (1), so there is a bit of a theorem to prove here.)
In most of the following special cases, we will consider only partitions, although it would be possible also to consider more general $\sigma$-algebras.
Recall that a discrete probability space is a set $X$ equipped with a function $\mu\colon X \to ]0,1]$ such that $\sum_{i \in X} \mu(i) = 1$; since $\mu(i) \gt 0$ is possible for only countably many $i$, $X$ must be countable. We make $X$ into a measure space (with every subset measurable) by defining $\mu(A) \coloneqq \sum_{i \in A} \mu(i)$. Since every inhabited set has positive measure, every almost-partition of $X$ is a partition; since every set is measurable, any partition is measurable.
Given a discrete probability space $(X,\mu)$ and a partition $\mathcal{P}$ of $X$, the entropy of $\mathcal{P}$ with respect to $\mu$ is defined to be the entropy of $\mathcal{P}$ with respect to the probability measure induced by $\mu$. Simplifying (2), we find
More specially, the entropy of the discrete probability space $(X,\mu)$ is the entropy of the partition of $X$ into singletons; we find
This is actually a special case of the entropy of a probability space, since the $\sigma$-algebra generated by the singletons is the power set of $X$.
Yet more specially, the entropy of a finite set $X$ is the entropy of $X$ equipped with the uniform discrete probability measure; we find
which is probably the best known mathematical formula for entropy, due to Max Planck, who attributed it to Ludwig Boltzmann. (Its physical interpretation appears below.)
Of all probability measures on $X$, the uniform measure has the maximum entropy?.
Recall that a Borel measure? $\mu$ on an interval $X$ in the real line is absolutely continuous if $\mu(A) = 0$ whenever $A$ is a null set (with respect to Lebesgue measure), or better such that $\mu(A) \gt 0$ whenever the Lebesgue measure of $A$ is positive. In this case, we can take the Radon–Nikodym derivative of $\mu$ with respect to Lebesgue measure, to get an integrable function $f$, called the probability distribution function; we recover $\mu$ by
where the integral is taken with respect to Lebesgue measure.
If $\mathcal{P}$ is a partition (or a Lebesgue-almost-partition) of an interval $X$ into Borel sets, then the entropy of $\mathcal{P}$ with respect to an integrable function $f$ is the entropy of $\mathcal{P}$ with respect to the measure induced by $f$ using the integral formula (4); we find
On the other hand, the entropy of the probability distribution space $(X,f)$ is the entropy of the entire $\sigma$-algebra of all Borel sets (which is not generated by a partition) with respect to $f$; we find
by a fairly complicated argument.
I haven't actually managed to check this argument yet, although my memory tags it as a true fact. —Toby
In the analogy between classical physics and quantum physics, we move from probability distributions on a phase space to density operators on a Hilbert space.
So just as the entropy of a probability distribution $f$ is given by $- \int f \log f$, so the entropy of a density operator $\rho$ is
using the functional calculus.
These are both special cases of the entropy of a state on a $C^*$-algebra.
There is a way to fit this into the framework given by (1), but I don't remember it (and never really understood it).
For two finite probability distributions $p$ and $q$, their relative entropy is
Or alternatively, for $\rho, \phi$ two density matrices, their relative entropy is
There is a generalization of these definitions to states on general von Neumann algebras, due to (Araki).
For more on this see relative entropy.
As hinted above, any probability distribution on a phase space in classical physics has an entropy, and any density matrix on a Hilbert space in quantum physics has an entropy. However, these are microscopic entropy, which is not the usual entropy in thermodynamics and most other branches of physics. (In particular, microscopic entropy is conserved, rather than increasing with time.)
Instead, physicists use coarse-grained entropy, which corresponds mathematically to taking the entropy of a $\sigma$-algebra much smaller than the $\sigma$-algebra of all measurable sets. Given a classical system with $N$ microscopic degrees of freedom, we identify $n$ macroscopic degrees of freedom that we can reasonably expect to measure, giving a map from $\mathbb{R}^N$ to $\mathbb{R}^n$ (or more generally, a map from an $N$-dimensional microscopic phase space to an $n$-dimensional macroscopic phase space). Then the $\sigma$-algebra of all measurable sets in $\mathbb{R}^n$ pulls back to a $\sigma$-algebra on $\mathbb{R}^N$, and the macroscopic entropy of a statistical state is the conditional entropy? of this $\sigma$-algebra. (Typically, $N$ is on the order of Avogadro's number, while $n$ is rarely more than half a dozen, and often as small as $2$.)
If we specify a state by a point in $\mathbb{R}^n$, a macroscopic pure state, and assume a uniform probability distribution on its fibre in $\mathbb{R}^N$, then this results in the maximum entropy?. If this fibre were a finite set, then we would recover Boltzmann's formula (3). This is never exactly true in classical statistical physics, but it is often nevertheless a very good approximation. (Boltzmann's formula actually makes better physical sense in quantum statistical physics, even though Boltzmann himself did not live to see this.)
A more sophisticated approach (pioneered by Josiah Gibbs?) is to consider all possible mixed microstates (that is all possible probability distributions on the space $\mathbb{R}^N$ of pure microstates) whose expectation values of total energy and other extensive quantities (among those that are functions of the macrostate) match the given pure macrostate (point in $\mathbb{R}^n$). We pick the mixed microstate with the maximum entropy?. If this is a thermal state?, then we say that the macrostate has a temperature, but it has an entropy in any case.
gravitational entropy
The concept of entropy was introduced, by Rudolf Clausius in 1865, in the context of physics, and then adapted to information theory by Claude Shannon in 1948, to quantum mechanics by John von Neumann in 1955, to ergodic theory by Andrey Kolmogorov and Sinai in 1958, and to topological dynamics by Adler, Konheim and McAndrew in 1965.
Relative entropy of states on von Neumann algebras was introduced in
Survey with an eye towards black hole entropy includes
A survey of entropy in operator algebras is in
See also
A large collection of references on quantum entropy is in
A discussion of entropy with an eye towards the presheaf topos over the site of finite measure spaces is in
Mikhail Gromov, In a Search for a Structure, Part I: On Entropy (2012) (pdf)
William Lawvere, State categories, closed categories, and the existence (subtitle: Semi-continuous entropy functions), IMA reprint 86, pdf
After the concept of entropy proved enormously useful in practice, many people have tried to find a more abstract foundation for the concept (and its variants) by characterizing it as the unique measure satisfying some list of plausible-sounding axioms.
A characterization of relative entropy on finite-dimensional C-star algebras is given in
A simple characterization of von Neumann entropy of density matrices (mixed quantum states) is discussed in
Entropy-like quantities appear in the study of many PDEs, with entropy estimates. For an intro see
Last revised on February 11, 2019 at 10:44:01. See the history of this page for a list of all contributions to it.