In mathematics, a vector measure is a function defined on a family of sets and taking vector values satisfying certain properties. It is a generalization of the concept of measure, which takes nonnegative real values only.
Definitions and first consequences
A vector measure is called countably additive if for any sequence of disjoint sets in such that their union is in it holds that
It can be proved that an additive vector measure is countably additive if and only if for any sequence as above one has
where is the norm on
Countably additive vector measures defined on sigma-algebras are more general than measures, signed measures, and complex measures, which are countably additive functions taking values respectively on the extended interval the set of real numbers, and the set of complex numbers.
Consider the field of sets made up of the interval together with the family of all Lebesgue measurable sets contained in this interval. For any such set , define
where is the indicator function of Depending on where is declared to take values, we get two different outcomes.
- viewed as a function from to the Lp-space is a vector measure which is not countably-additive.
- viewed as a function from to the Lp-space is a countably-additive vector measure.
Both of these statements follow quite easily from the criterion (*) stated above.
The variation of a vector measure
Given a vector measure the variation of is defined as
of into a finite number of disjoint sets, for all in . Here, is the norm on
The variation of is a finitely additive function taking values in It holds that
for any in If is finite, the measure is said to be of bounded variation. One can prove that if is a vector measure of bounded variation, then is countably additive if and only if is countably additive.
In the theory of vector measures, Lyapunov's theorem states that the range of a (non-atomic) vector measure is closed and convex. In fact, the range of a non-atomic vector measure is a zonoid (the closed and convex set that is the limit of a convergent sequence of zonotopes). It is used in economics, in ("bang–bang") control theory, and in statistical theory. Lyapunov's theorem has been proved by using the Shapley–Folkman lemma, which has been viewed as a discrete analogue of Lyapunov's theorem. 
- Kluvánek, I., Knowles, G., Vector Measures and Control Systems, North-Holland Mathematics Studies 20, Amsterdam, 1976.
- Diestel, Joe; Uhl, Jerry J., Jr. (1977). Vector measures. Providence, R.I: American Mathematical Society. ISBN 0-8218-1515-6.
- Rolewicz, Stefan (1987). Functional analysis and control theory: Linear systems. Mathematics and its Applications (East European Series) 29 (Translated from the Polish by Ewa Bednarczuk ed.). Dordrecht; Warsaw: D. Reidel Publishing Co.; PWN—Polish Scientific Publishers. pp. xvi+524. ISBN 90-277-2186-6. MR 920371. OCLC 13064804
- Roberts, John (July 1986). "Large economies". In David M. Kreps; John Roberts; Robert B. Wilson. Contributions to the New Palgrave. Research paper 892. Palo Alto, CA: Graduate School of Business, Stanford University. pp. 30–35. (Draft of articles for the first edition of New Palgrave Dictionary of Economics). Retrieved 7 February 2011
- Aumann, Robert J. (January 1966). "Existence of competitive equilibrium in markets with a continuum of traders". Econometrica 34 (1): 1–17. JSTOR 1909854. MR 191623. This paper builds on two papers by Aumann:
- Vind, Karl (May 1964). "Edgeworth-allocations in an exchange economy with many traders". International Economic Review 5 (2). pp. 165–77. JSTOR 2525560. Vind's article was noted by Debreu (1991, p. 4) with this comment:
The concept of a convex set (i.e., a set containing the segment connecting any two of its points) had repeatedly been placed at the center of economic theory before 1964. It appeared in a new light with the introduction of integration theory in the study of economic competition: If one associates with every agent of an economy an arbitrary set in the commodity space and if one averages those individual sets over a collection of insignificant agents, then the resulting set is necessarily convex. [Debreu appends this footnote: "On this direct consequence of a theorem of A. A. Lyapunov, see Vind (1964)."] But explanations of the ... functions of prices ... can be made to rest on the convexity of sets derived by that averaging process. Convexity in the commodity space obtained by aggregation over a collection of insignificant agents is an insight that economic theory owes ... to integration theory. [Italics added]
Debreu, Gérard (March 1991). "The Mathematization of economic theory". The American Economic Review. 81, number 1 (Presidential address delivered at the 103rd meeting of the American Economic Association, 29 December 1990, Washington, DC). pp. 1–7. JSTOR 2006785.
- Hermes, Henry; LaSalle, Joseph P. (1969). Functional analysis and time optimal control. Mathematics in Science and Engineering 56. New York—London: Academic Press. pp. viii+136. MR 420366.
- Artstein, Zvi (1980). "Discrete and continuous bang-bang and facial spaces, or: Look for the extreme points". SIAM Review 22 (2). pp. 172–185. doi:10.1137/1022026. JSTOR 2029960. MR 564562.
- Tardella, Fabio (1990). "A new proof of the Lyapunov convexity theorem". SIAM Journal on Control and Optimization 28 (2). pp. 478–481. doi:10.1137/0328026. MR 1040471.
- Starr, Ross M. (2008). "Shapley–Folkman theorem". In Durlauf, Steven N.; Blume, Lawrence E., ed. The New Palgrave Dictionary of Economics (Second ed.). Palgrave Macmillan. pp. 317–318 (1st ed.). doi:10.1057/9780230226203.1518.
- Page 210: Mas-Colell, Andreu (1978). "A note on the core equivalence theorem: How many blocking coalitions are there?". Journal of Mathematical Economics 5 (3). pp. 207–215. doi:10.1016/0304-4068(78)90010-1. MR 514468.
- Cohn, Donald L. (1997) . Measure theory (reprint ed.). Boston–Basel–Stuttgart: Birkhäuser Verlag. pp. IX+373. ISBN 3-7643-3003-1. Zbl 0436.28001
- Diestel, Joe; Uhl, Jerry J., Jr. (1977). Vector measures. Mathematical Surveys 15. Providence, R.I: American Mathematical Society. pp. xiii+322. ISBN 0-8218-1515-6.
- Kluvánek, I., Knowles, G, Vector Measures and Control Systems, North-Holland Mathematics Studies 20, Amsterdam, 1976.
- van Dulst, D. (2001), "Vector measures", in Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4
- Rudin, W (1973). Functional analysis. New York: McGraw-Hill. p. 114.