Absolutely and completely monotonic functions and sequences

Last updated

In mathematics, the notions of an absolutely monotonic function and a completely monotonic function are two very closely related concepts. Both imply very strong monotonicity properties. Both types of functions have derivatives of all orders. In the case of an absolutely monotonic function, the function as well as its derivatives of all orders must be non-negative in its domain of definition which would imply that the function as well as its derivatives of all orders are monotonically increasing functions in the domain of definition. In the case of a completely monotonic function, the function and its derivatives must be alternately non-negative and non-positive in its domain of definition which would imply that function and its derivatives are alternately monotonically increasing and monotonically decreasing functions.

Contents

Such functions were first studied by S. Bernshtein in 1914 and the terminology is also due to him. [1] [2] [3] There are several other related notions like the concepts of almost completely monotonic function, logarithmically completely monotonic function, strongly logarithmically completely monotonic function, strongly completely monotonic function and almost strongly completely monotonic function. [4] [5] Another related concept is that of a completely/absolutely monotonic sequence. This notion was introduced by Hausdorff in 1921.

The notions of completely and absolutely monotone function/sequence play an important role in several areas of mathematics. For example, in classical analysis they occur in the proof of the positivity of integrals involving Bessel functions or the positivity of Cesàro means of certain Jacobi series. [6] Such functions occur in other areas of mathematics such as probability theory, numerical analysis, and elasticity. [7]

Definitions

Functions

A real valued function defined over an interval in the real line is called an absolutely monotonic function if it has derivatives of all orders and for all in . [1] The function is called a completely monotonic function if for all in . [1]

The two notions are mutually related. The function is completely monotonic if and only if is absolutely monotonic on where the interval obtained by reflecting with respect to the origin. (Thus, if is the interval then is the interval .)

In applications, the interval on the real line that is usually considered is the closed-open right half of the real line, that is, the interval .

Examples

The following functions are absolutely monotonic in the specified regions. [8] :142–143

  1. , where a non-negative constant, in the region
  2. , where for all , in the region
  3. in the region
  4. in the region

Sequences

A sequence is called an absolutely monotonic sequence if its elements are non-negative and its successive differences are all non-negative, that is, if

where .

A sequence is called a completely monotonic sequence if its elements are non-negative and its successive differences are alternately non-positive and non-negative, [8] :101 that is, if

Examples

The sequences and for are completely monotonic sequences.

Some important properties

Both the extensions and applications of the theory of absolutely monotonic functions derive from theorems.

where is non-decreasing and bounded on .
The determination of this function from the sequence is referred to as the Hausdorff moment problem.

Further reading

The following is a random selection from the large body of literature on absolutely/completely monotonic functions/sequences.

See also

Related Research Articles

<span class="mw-page-title-main">Measure (mathematics)</span> Generalization of mass, length, area and volume

In mathematics, the concept of a measure is a generalization and formalization of geometrical measures and other common notions, such as magnitude, mass, and probability of events. These seemingly distinct concepts have many similarities and can often be treated together in a single mathematical context. Measures are foundational in probability theory, integration theory, and can be generalized to assume negative values, as with electrical charge. Far-reaching generalizations of measure are widely used in quantum physics and physics in general.

In mathematics, the classic Möbius inversion formula is a relation between pairs of arithmetic functions, each defined from the other by sums over divisors. It was introduced into number theory in 1832 by August Ferdinand Möbius.

In mathematics, the branch of real analysis studies the behavior of real numbers, sequences and series of real numbers, and real functions. Some particular properties of real-valued sequences and functions that real analysis studies include convergence, limits, continuity, smoothness, differentiability and integrability.

<span class="mw-page-title-main">Monotonic function</span> Order-preserving mathematical function

In mathematics, a monotonic function is a function between ordered sets that preserves or reverses the given order. This concept first arose in calculus, and was later generalized to the more abstract setting of order theory.

In the mathematical field of real analysis, the monotone convergence theorem is any of a number of related theorems proving the good convergence behaviour of monotonic sequences, i.e. sequences that are non-increasing, or non-decreasing. In its simplest form, it says that a non-decreasing bounded-above sequence of real numbers converges to its smallest upper bound, its supremum. Likewise, a non-increasing bounded-below sequence converges to its largest lower bound, its infimum. In particular, infinite sums of non-negative numbers converge to the supremum of the partial sums if and only if the partial sums are bounded.

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 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, the Radon–Nikodym theorem is a result in measure theory that expresses the relationship between two measures defined on the same measurable space. A measure is a set function that assigns a consistent magnitude to the measurable subsets of a measurable space. Examples of a measure include area and volume, where the subsets are sets of points; or the probability of an event, which is a subset of possible outcomes within a wider probability space.

In measure theory, Lebesgue's dominated convergence theorem gives a mild sufficient condition under which limits and integrals of a sequence of functions can be interchanged. More technically it says that if a sequence of functions is bounded in absolute value by an integrable function and is almost everywhere point wise convergent to a function then the sequence convergences in to its point wise limit, and in particular the integral of the limit is the limit of the integrals. Its power and utility are two of the primary theoretical advantages of Lebesgue integration over Riemann integration.

Renewal theory is the branch of probability theory that generalizes the Poisson process for arbitrary holding times. Instead of exponentially distributed holding times, a renewal process may have any independent and identically distributed (IID) holding times that have finite mean. A renewal-reward process additionally has a random sequence of rewards incurred at each holding time, which are IID but need not be independent of the holding times.

In mathematics, the Hausdorff moment problem, named after Felix Hausdorff, asks for necessary and sufficient conditions that a given sequence (m0, m1, m2, ...) be the sequence of moments

In real analysis, a branch of mathematics, Bernstein's theorem states that every real-valued function on the half-line [0, ∞) that is totally monotone is a mixture of exponential functions. In one important special case the mixture is a weighted average, or expected value.

In mathematics, convergence tests are methods of testing for the convergence, conditional convergence, absolute convergence, interval of convergence or divergence of an infinite series .

In mathematics, uniform integrability is an important concept in real analysis, functional analysis and measure theory, and plays a vital role in the theory of martingales.

In mathematics, in particular in measure theory, a content is a real-valued function defined on a collection of subsets such that

In real analysis and measure theory, the Vitali convergence theorem, named after the Italian mathematician Giuseppe Vitali, is a generalization of the better-known dominated convergence theorem of Henri Lebesgue. It is a characterization of the convergence in Lp in terms of convergence in measure and a condition related to uniform integrability.

In mathematical analysis, and especially in real, harmonic analysis and functional analysis, an Orlicz space is a type of function space which generalizes the Lp spaces. Like the Lp spaces, they are Banach spaces. The spaces are named for Władysław Orlicz, who was the first to define them in 1932.

In the mathematical field of analysis, a well-known theorem describes the set of discontinuities of a monotone real-valued function of a real variable; all discontinuities of such a (monotone) function are necessarily jump discontinuities and there are at most countably many of them.

In mathematics, especially measure theory, a set function is a function whose domain is a family of subsets of some given set and that (usually) takes its values in the extended real number line which consists of the real numbers and

<span class="mw-page-title-main">Lebesgue integration</span> Method of integration

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, extends the integral to a larger class of functions. It also extends the domains on which these functions can be defined.

References

  1. 1 2 3 "Absolutely monotonic function". encyclopediaofmath.org. Encyclopedia of Mathematics. Retrieved 28 December 2023.
  2. S. Bernstein (1914). "Sur la définition et les propriétés des fonctions analytique d'une variable réelle". Mathematische Annalen. 75 (4): 449–468. doi:10.1007/BF01563654.
  3. S. Bernstein (1928). "Sur les fonctions absolument monotones". Acta Mathematica. 52: 1–66. doi:10.1007/BF02592679.
  4. Guo, Senlin (2017). "Some Properties of Functions Related to Completely Monotonic Functions" (PDF). Filomat. 31 (2): 247–254. doi:10.2298/FIL1702247G . Retrieved 29 December 2023.
  5. Guo, Senlin; Laforgia, Andrea; Batir, Necdet; Luo, Qiu-Ming (2014). "Completely Monotonic and Related Functions: Their Applications" (PDF). Journal of Applied Mathematics. 2014: 1–3. doi: 10.1155/2014/768516 . Retrieved 28 December 2023.
  6. R. Askey (1973). "Summability of Jacobi series". Transactions of the American Mathematical Society. 179: 71–84. doi:10.1090/S0002-9947-1973-0315351-7.
  7. William Feller (1971). An Introduction to Probability Theory and Its Applications, Vol. 2 (3 ed.). New York: Wiley. ISBN   9780471257097. OCLC   279852.
  8. 1 2 Widder, David Vernon (1946). The Laplace Transform. Princeton University Press. ISBN   9780486477558. OCLC   630478002.