A product integral is any product-based counterpart of the usual sum-based integral of calculus. The product integral was developed by the mathematician Vito Volterra in 1887 to solve systems of linear differential equations. [1] [2]
The classical Riemann integral of a function can be defined by the relation
where the limit is taken over all partitions of the interval whose norms approach zero. Product integrals are similar, but take the limit of a product instead of the limit of a sum. They can be thought of as "continuous" versions of "discrete" products. They are defined as
For the case of , the product integral reduces exactly to the case of Lebesgue integration, that is, to classical calculus. Thus, the interesting cases arise for functions where is either some commutative algebra, such as a finite-dimensional matrix field, or if is a non-commutative algebra. The theories for these two cases, the commutative and non-commutative cases, have little in common. The non-commutative case is far more complicated; it requires proper path-ordering to make the integral well-defined.
For the commutative case, three distinct definitions are commonplace in the literature, referred to as Type-I, Type-II or geometric, and type-III or bigeometric. [3] [4] [5] Such integrals have found use in epidemiology (the Kaplan–Meier estimator) and stochastic population dynamics. The geometric integral, together with the geometric derivative, is useful in image analysis [6] and in the study of growth/decay phenomena (e.g., in economic growth, bacterial growth, and radioactive decay). [7] [8] The bigeometric integral, together with the bigeometric derivative, is useful in some applications of fractals, [9] [10] [11] [12] and in the theory of elasticity in economics. [3] [5] [13]
The non-commutative case commonly arises in quantum mechanics and quantum field theory. The integrand is generally an operator belonging to some non-commutative algebra. In this case, one must be careful to establish a path-ordering while integrating. A typical result is the ordered exponential. The Magnus expansion provides one technique for computing the Volterra integral. Examples include the Dyson expansion, the integrals that occur in the operator product expansion and the Wilson line, a product integral over a gauge field. The Wilson loop is the trace of a Wilson line. The product integral also occurs in control theory, as the Peano–Baker series describing state transitions in linear systems written in a master equation type form.
The Volterra product integral is most useful when applied to matrix-valued functions or functions with values in a Banach algebra. When applied to scalars belonging to a non-commutative field, to matrixes, and to operators, i.e. to mathematical objects that don't commute, the Volterra integral splits in two definitions. [14]
The left product integral is
With this notation of left products (i.e. normal products applied from left)
The right product integral
With this notation of right products (i.e. applied from right)
Where is the identity matrix and D is a partition of the interval [a,b] in the Riemann sense, i.e. the limit is over the maximum interval in the partition. Note how in this case time ordering becomes evident in the definitions.
The Magnus expansion provides a technique for computing the product integral. It defines a continuous-time version of the Baker–Campbell–Hausdorff formula.
The product integral satisfies a collection of properties defining a one-parameter continuous group; these are stated in two articles showing applications: the Dyson series and the Peano–Baker series.
The commutative case is vastly simpler, and, as a result, a large variety of distinct notations and definitions have appeared. Three distinct styles are popular in the literature. This subsection adopts the product notation for product integration instead of the integral (usually modified by a superimposed times symbol or letter P) favoured by Volterra and others. An arbitrary classification of types is adopted to impose some order in the field.
When the function to be integrated is valued in the real numbers, then the theory reduces exactly to the theory of Lebesgue integration.
The type I product integral corresponds to Volterra's original definition. [2] [15] [16] The following relationship exists for scalar functions :
which is called the geometric integral. The logarithm is well-defined if f takes values in the real or complex numbers, or if f takes values in a commutative field of commuting trace-class operators. This definition of the product integral is the continuous analog of the discrete product operator (with ) and the multiplicative analog to the (normal/standard/additive) integral (with ):
It is very useful in stochastics, where the log-likelihood (i.e. the logarithm of a product integral of independent random variables) equals the integral of the logarithm of these (infinitesimally many) random variables:
The type III product integral is called the bigeometric integral.
For the commutative case, the following results hold for the type II product integral (the geometric integral).
The geometric integral (type II above) plays a central role in the geometric calculus, [3] [4] [17] which is a multiplicative calculus. The inverse of the geometric integral, which is the geometric derivative, denoted , is defined using the following relationship:
Thus, the following can be concluded:
where X is a random variable with probability distribution F(x).
Compare with the standard law of large numbers:
When the integrand takes values in the real numbers, then the product intervals become easy to work with by using simple functions. Just as in the case of Lebesgue version of (classical) integrals, one can compute product integrals by approximating them with the product integrals of simple functions. The case of Type II geometric integrals reduces to exactly the case of classical Lebesgue integration.
Because simple functions generalize step functions, in what follows we will only consider the special case of simple functions that are step functions. This will also make it easier to compare the Lebesgue definition with the Riemann definition.
Given a step function with a tagged partition
one approximation of the "Riemann definition" of the type I product integral is given by [18]
The (type I) product integral was defined to be, roughly speaking, the limit of these products by Ludwig Schlesinger in a 1931 article.[ which? ]
Another approximation of the "Riemann definition" of the type I product integral is defined as
When is a constant function, the limit of the first type of approximation is equal to the second type of approximation. [19] Notice that in general, for a step function, the value of the second type of approximation doesn't depend on the partition, as long as the partition is a refinement of the partition defining the step function, whereas the value of the first type of approximation does depend on the fineness of the partition, even when it is a refinement of the partition defining the step function.
It turns out that [20] for any product-integrable function , the limit of the first type of approximation equals the limit of the second type of approximation. Since, for step functions, the value of the second type of approximation doesn't depend on the fineness of the partition for partitions "fine enough", it makes sense to define [21] the "Lebesgue (type I) product integral" of a step function as
where is the tagged partition corresponding to the step function . (In contrast, the corresponding quantity would not be unambiguously defined using the first type of approximation.)
This generalizes to arbitrary measure spaces readily. If is a measure space with measure , then for any product-integrable simple function (i.e. a conical combination of the indicator functions for some disjoint measurable sets ), its type I product integral is defined to be
since is the value of at any point of . In the special case where , is Lebesgue measure, and all of the measurable sets are intervals, one can verify that this is equal to the definition given above for that special case. Analogous to the theory of Lebesgue (classical) integrals, the Type I product integral of any product-integrable function can be written as the limit of an increasing sequence of Volterra product integrals of product-integrable simple functions.
Taking logarithms of both sides of the above definition, one gets that for any product-integrable simple function :
where we used the definition of integral for simple functions. Moreover, because continuous functions like can be interchanged with limits, and the product integral of any product-integrable function is equal to the limit of product integrals of simple functions, it follows that the relationship
holds generally for any product-integrable . This clearly generalizes the property mentioned above.
The Type I integral is multiplicative as a set function, [22] which can be shown using the above property. More specifically, given a product-integrable function one can define a set function by defining, for every measurable set ,
where denotes the indicator function of . Then for any two disjoint measurable sets one has
This property can be contrasted with measures, which are sigma-additive set functions.
However, the Type I integral is not multiplicative as a functional. Given two product-integrable functions , and a measurable set , it is generally the case that
If is a measure space with measure , then for any product-integrable simple function (i.e. a conical combination of the indicator functions for some disjoint measurable sets ), its type II product integral is defined to be
This can be seen to generalize the definition given above.
Taking logarithms of both sides, we see that for any product-integrable simple function :
where the definition of the Lebesgue integral for simple functions was used. This observation, analogous to the one already made for Type II integrals above, allows one to entirely reduce the "Lebesgue theory of type II geometric integrals" to the Lebesgue theory of (classical) integrals. In other words, because continuous functions like and can be interchanged with limits, and the product integral of any product-integrable function is equal to the limit of some increasing sequence of product integrals of simple functions, it follows that the relationship
holds generally for any product-integrable . This generalizes the property of geometric integrals mentioned above.
In probability theory, the expected value is a generalization of the weighted average. Informally, the expected value is the mean of the possible values a random variable can take, weighted by the probability of those outcomes. Since it is obtained through arithmetic, the expected value sometimes may not even be included in the sample data set; it is not the value you would "expect" to get in reality.
In theoretical physics, a Feynman diagram is a pictorial representation of the mathematical expressions describing the behavior and interaction of subatomic particles. The scheme is named after American physicist Richard Feynman, who introduced the diagrams in 1948. The interaction of subatomic particles can be complex and difficult to understand; Feynman diagrams give a simple visualization of what would otherwise be an arcane and abstract formula. According to David Kaiser, "Since the middle of the 20th century, theoretical physicists have increasingly turned to this tool to help them undertake critical calculations. Feynman diagrams have revolutionized nearly every aspect of theoretical physics." While the diagrams are applied primarily to quantum field theory, they can also be used in other areas of physics, such as solid-state theory. Frank Wilczek wrote that the calculations that won him the 2004 Nobel Prize in Physics "would have been literally unthinkable without Feynman diagrams, as would [Wilczek's] calculations that established a route to production and observation of the Higgs particle."
In mathematics, an integral is the continuous analog of a sum, which is used to calculate areas, volumes, and their generalizations. Integration, the process of computing an integral, is one of the two fundamental operations of calculus, the other being differentiation. Integration was initially used to solve problems in mathematics and physics, such as finding the area under a curve, or determining displacement from velocity. Usage of integration expanded to a wide variety of scientific fields thereafter.
In the branch of mathematics known as real analysis, the Riemann integral, created by Bernhard Riemann, was the first rigorous definition of the integral of a function on an interval. It was presented to the faculty at the University of Göttingen in 1854, but not published in a journal until 1868. For many functions and practical applications, the Riemann integral can be evaluated by the fundamental theorem of calculus or approximated by numerical integration, or simulated using Monte Carlo integration.
In mathematical analysis, the Haar measure assigns an "invariant volume" to subsets of locally compact topological groups, consequently defining an integral for functions on those groups.
In calculus, and more generally in mathematical analysis, integration by parts or partial integration is a process that finds the integral of a product of functions in terms of the integral of the product of their derivative and antiderivative. It is frequently used to transform the antiderivative of a product of functions into an antiderivative for which a solution can be more easily found. The rule can be thought of as an integral version of the product rule of differentiation; it is indeed derived using the product rule.
In mathematical analysis, Hölder's inequality, named after Otto Hölder, is a fundamental inequality between integrals and an indispensable tool for the study of Lp spaces.
In mathematics, a Gaussian function, often simply referred to as a Gaussian, is a function of the base form and with parametric extension for arbitrary real constants a, b and non-zero c. It is named after the mathematician Carl Friedrich Gauss. The graph of a Gaussian is a characteristic symmetric "bell curve" shape. The parameter a is the height of the curve's peak, b is the position of the center of the peak, and c controls the width of the "bell".
In calculus and real analysis, absolute continuity is a smoothness property of functions that is stronger than continuity and uniform continuity. The notion of absolute continuity allows one to obtain generalizations of the relationship between the two central operations of calculus—differentiation and integration. This relationship is commonly characterized in the framework of Riemann integration, but with absolute continuity it may be formulated in terms of Lebesgue integration. For real-valued functions on the real line, two interrelated notions appear: absolute continuity of functions and absolute continuity of measures. These two notions are generalized in different directions. The usual derivative of a function is related to the Radon–Nikodym derivative, or density, of a measure. We have the following chains of inclusions for functions over a compact subset of the real line:
In mathematical analysis, Fubini's theorem characterizes the conditions under which it is possible to compute a double integral by using an iterated integral. It was introduced by Guido Fubini in 1907. The theorem states that if a function is Lebesgue integrable on a rectangle , then one can evaluate the double integral as an iterated integral: This formula is generally not true for the Riemann integral, but it is true if the function is continuous on the rectangle. In multivariable calculus, this weaker result is sometimes also called Fubini's theorem, although it was already known by Leonhard Euler.
In mathematics, Fatou's lemma establishes an inequality relating the Lebesgue integral of the limit inferior of a sequence of functions to the limit inferior of integrals of these functions. The lemma is named after Pierre Fatou.
In mathematics, Jensen's inequality, named after the Danish mathematician Johan Jensen, relates the value of a convex function of an integral to the integral of the convex function. It was proved by Jensen in 1906, building on an earlier proof of the same inequality for doubly-differentiable functions by Otto Hölder in 1889. Given its generality, the inequality appears in many forms depending on the context, some of which are presented below. In its simplest form the inequality states that the convex transformation of a mean is less than or equal to the mean applied after convex transformation.
Functional integration is a collection of results in mathematics and physics where the domain of an integral is no longer a region of space, but a space of functions. Functional integrals arise in probability, in the study of partial differential equations, and in the path integral approach to the quantum mechanics of particles and fields.
In statistics and information theory, a maximum entropy probability distribution has entropy that is at least as great as that of all other members of a specified class of probability distributions. According to the principle of maximum entropy, if nothing is known about a distribution except that it belongs to a certain class, then the distribution with the largest entropy should be chosen as the least-informative default. The motivation is twofold: first, maximizing entropy minimizes the amount of prior information built into the distribution; second, many physical systems tend to move towards maximal entropy configurations over time.
In the mathematical field of real analysis, a simple function is a real -valued function over a subset of the real line, similar to a step function. Simple functions are sufficiently "nice" that using them makes mathematical reasoning, theory, and proof easier. For example, simple functions attain only a finite number of values. Some authors also require simple functions to be measurable; as used in practice, they invariably are.
In mathematics, the Bochner integral, named for Salomon Bochner, extends the definition of Lebesgue integral to functions that take values in a Banach space, as the limit of integrals of simple functions.
The concept of an abstract Wiener space is a mathematical construction developed by Leonard Gross to understand the structure of Gaussian measures on infinite-dimensional spaces. The construction emphasizes the fundamental role played by the Cameron–Martin space. The classical Wiener space is the prototypical example.
In mathematics, the Pettis integral or Gelfand–Pettis integral, named after Israel M. Gelfand and Billy James Pettis, extends the definition of the Lebesgue integral to vector-valued functions on a measure space, by exploiting duality. The integral was introduced by Gelfand for the case when the measure space is an interval with Lebesgue measure. The integral is also called the weak integral in contrast to the Bochner integral, which is the strong integral.
In mathematics, the integral of a non-negative function of a single variable can be regarded, in the simplest case, as the area between the graph of that function and the X axis. The Lebesgue integral, named after French mathematician Henri Lebesgue, is one way to make this concept rigorous and to extend it to more general functions.
A product distribution is a probability distribution constructed as the distribution of the product of random variables having two other known distributions. Given two statistically independent random variables X and Y, the distribution of the random variable Z that is formed as the product is a product distribution.