Balanced set
In linear algebra and related areas of mathematics a balanced set, circled set or disk in a vector space (over a field 𝕂 with an absolute value function ) is a set S such that aS ⊆ S for all scalars a satisfying |a| ≤ 1.
The balanced hull or balanced envelope of a set S is the smallest balanced set containing S. The balanced core of a subset S is the largest balanced set contained in S.
Definition
Suppose that X is a vector space over the field 𝕂 of real or complex numbers. Elements of 𝕂 are called scalars.
Notation: If S is a set, a is a scalar, and B ⊆ 𝕂 then let a S := { a s : s ∈ S } and B S := { b s : b ∈ B, s ∈ S }.
Notation: For any 0 ≤ r ≤ ∞, let Br := { a ∈ 𝕂 : |a| ≤ r } denote the closed ball of radius r in 𝕂 centered at 0 and let Br := { a ∈ 𝕂 : |a| < r } denote the corresponding open ball. Note that B0 = ∅, B0 = { 0 }, and B∞ = B∞ = 𝕂.
- Observe that every balanced subset of the field 𝕂 is of the form Br or Br for some 0 ≤ r ≤ ∞.
Definition: A subset S of X is called balanced if it satisfies any of the following equivalent conditions:
- a S ⊆ S for all scalars a satisfying |a| ≤ 1;
- B1 S ⊆ S, where B1 := { a ∈ 𝕂 : |a| ≤ 1 };
- S = B1 S;[1]
- For every s ∈ S, S ∩ 𝕂 s = B1 (S ∩ 𝕂 s);
- Note that if we let R := S ∩ 𝕂 s then the above equality becomes R = B1 R, which is exactly the previous condition for a set to be balanced. Thus, S is balanced if and only if for every s ∈ S, S ∩ 𝕂 s is a balanced set (according to any of the previous defining conditions);
- For every 1-dimensional vector subspace Y of span S, S ∩ Y is a balanced set (according to any defining condition over than this one).
- For every s ∈ S, there exists some 0 ≤ r ≤ ∞ such that S ∩ 𝕂 s = Br s or S ∩ 𝕂 s = Br s;
If S is a convex set then we may add to this list:
- a S ⊆ S for all scalars a satisfying |a| = 1.[2]
If 𝕂 = ℝ then we may add to this list:
- S is symmetric (i.e. - S = S) and [0, 1) S ⊆ S.
Definition and notation: The balanced hull of a subset S of X, denoted by bal S, is defined in any of the following equivalent ways:
- bal S is the smallest balanced subset of X containing S;
- bal S is the intersection of all balanced sets containing S;
- bal S = (a S);
- bal S = B1 S, where B1 := { a ∈ 𝕂 : |a| ≤ 1 }.[1]
Definition and notation: The balanced core of a subset S of X, denoted by balcore S, is defined in any of the following equivalent ways:
- balcore S is the largest balanced set contained in S;
- balcore S is the union of all balanced subsets of S;
- balcore S = ∅ if 0 ∉ S while balcore S = (aS) if 0 ∈ S.
Examples and sufficient conditions
- Sufficient conditions
- The closure of a balanced set is balanced.
- The convex hull of a balanced set is convex and balanced (i.e. absolutely convex).
- However, the balanced hull of a convex set may fail to be convex.
- The balanced hull of a compact (resp. totally bounded, bounded) set is compact (resp. totally bounded, bounded).[3]
- Arbitrary unions of balanced sets are a balanced set.
- Arbitrary intersections of balanced sets are a balanced set.
- Scalar multiples of balanced sets are balanced.
- The Minkowski sum of two balanced sets is balanced.
- The image of a balanced set under a linear operator is again a balanced set.
- The inverse image of a balanced set (in the codomain) under a linear operator is again a balanced set (in the domain).
- In any topological vector space, the interior of a balanced neighborhood of 0 is again balanced.
- Examples
- If S ⊆ X is any subset and B1 := { a ∈ 𝕂 : |a| < 1 } then B1 S is a balanced set.
- In particular, if U ⊆ X is any balanced neighborhood of the origin in a TVS X then Int U = B1 U = a U ⊆ U.
- If 𝕂 is the field real or complex numbers and X = 𝕂 is the normed space over 𝕂 with the usual Euclidean norm, then the balanced subsets of X are exactly the following:[4]
- ∅
- X
- { 0 }
- { x ∈ X : |x| < r } for some real r > 0
- { x ∈ X : |x| ≤ r } for some real r > 0.
- The open and closed balls centered at 0 in a normed vector space are balanced sets.
- Any vector subspace of a real or complex vector space is a balanced set.
- If X = ℝ2 (X is a vector space over ℝ), B1 is the closed unit ball in X centered at the origin, x0 ∈ X is non-zero, and L := ℝx0, then the set R := B1 ∪ L is a closed, symmetric, and balanced neighborhood of the origin in X. More generally, if C is any closed subset of X such that (0, 1) C ⊆ C, then S := B1 ∪ C ∪ (- C) is a closed, symmetric, and balanced neighborhood of the origin in X. This example can be generalized to ℝn for any integer n ≥ 1.
- The cartesian product of a family of balanced sets is balanced in the product space of the corresponding vector spaces (over the same field 𝕂).
- Consider ℂ, the field of complex numbers, as a 1-dimensional vector space. The balanced sets are ℂ itself, the empty set and the open and closed discs centered at zero. Contrariwise, in the two dimensional Euclidean space there are many more balanced sets: any line segment with midpoint at the origin will do. As a result, ℂ and ℝ2 are entirely different as far as scalar multiplication is concerned.
- If p is a semi-norm on a linear space X, then for any constant c > 0 the set { x ∈ X : p(x) ≤ c } is balanced.
- Let X = ℝ2 and let B be the union of the line segment between (-1, 0) and (1, 0) and the line segment between (0, -1) and (0, 1). Then B is balanced but not convex or absorbing. However, span B = X.
- Let X = ℝ2 and for every 0 ≤ t < 𝜋, let rt be any positive real number and let Bt be the (open or closed) line segment between the points (cos t, sin t) and -(cos t, sin t). Then the set B = rt Bt is balanced and absorbing but it is not necessarily convex.
- The balanced hull of a closed set need not be closed. Take for instance the graph of xy = 1 in X = ℝ2.
Properties
- Properties of balanced sets
- A set is absolutely convex if and only if it is convex and balanced.
- If B is balanced then for any scalar a, aB = |a|B.
- If B is balanced then for any scalars a and b such that |a| ≤ |b|, aB ⊆ bB.
- The union of { 0 } and the interior of a balanced set is balanced.
- If B is a balanced subset of X, then B is absorbing in X if and only if for all x ∈ X, there exists r > 0 such that x ∈ rB.[2]
- If B is a balanced subset of X, then B is absorbing in span B.
- The Minkowski sum of two balanced sets is balanced.
- Every balanced set is symmetric.
- Every balanced set is path connected.
- Suppose B is balanced. If Y is a 1-dimensional vector subspace of X then B ∩ Y is convex and balanced. If Y is a 1-dimensional vector subspace of span B then B ∩ Y is also absorbing in Y.
- If B ≠ ∅ is a balanced then for any x ∈ X, B ∩ ℝ x is a convex balanced set containing the origin. If B is a neighborhood of 0 in X then B ∩ ℝ x is a convex balanced neighborhood of 0 in the real vector subspace ℝ x.
- Properties of balanced hulls
- a bal S = bal(aS) for any subset S of X and any scalar a.
- bal( S) = bal(S) for any collection 𝒮 of subsets of X.
- In any topological vector space, the balanced hull of any open neighborhood of 0 is again open.
- If X is a Hausdorff topological vector space and if K is a compact subset of X, then the balanced hull of K is compact.[5]
- Balanced core
- The balanced core of a closed subset is closed.
- The balanced core of a absorbing subset is absorbing.
See also
- Absolutely convex set
- Absorbing set – A set that can be "inflated" to eventually always include any given point in a space
- Bounded set (topological vector space)
- Convex set – In geometry, set that intersects every line into a single line segment
- Star domain
- Symmetric set
- Topological vector space – Vector space with a notion of nearness
References
- Swartz 1992, pp. 4-8.
- Narici & Beckenstein 2011, pp. 107-110.
- Narici & Beckenstein 2011, pp. 156-175.
- Jarchow 1981, p. 34.
- Trèves 2006, p. 56.
- Bourbaki, Nicolas (1987) [1981]. Sur certains espaces vectoriels topologiques [Topological Vector Spaces: Chapters 1–5]. Annales de l'Institut Fourier. Éléments de mathématique. 2. Translated by Eggleston, H.G.; Madan, S. Berlin New York: Springer-Verlag. ISBN 978-3-540-42338-6. OCLC 17499190.
- Conway, John (1990). A course in functional analysis. Graduate Texts in Mathematics. 96 (2nd ed.). New York: Springer-Verlag. ISBN 978-0-387-97245-9. OCLC 21195908.
- Dunford, Nelson; Schwartz, Jacob T. (1988). Linear Operators. Pure and applied mathematics. 1. New York: Wiley-Interscience. ISBN 978-0-471-60848-6. OCLC 18412261.
- Edwards, Robert E. (1995). Functional Analysis: Theory and Applications. New York: Dover Publications. ISBN 978-0-486-68143-6. OCLC 30593138.
- Jarchow, Hans (1981). Locally convex spaces. Stuttgart: B.G. Teubner. ISBN 978-3-519-02224-4. OCLC 8210342.
- Köthe, Gottfried (1969). Topological Vector Spaces I. Grundlehren der mathematischen Wissenschaften. 159. Translated by Garling, D.J.H. New York: Springer Science & Business Media. ISBN 978-3-642-64988-2. MR 0248498. OCLC 840293704.
- Köthe, Gottfried (1979). Topological Vector Spaces II. Grundlehren der mathematischen Wissenschaften. 237. New York: Springer Science & Business Media. ISBN 978-0-387-90400-9. OCLC 180577972.
- Narici, Lawrence; Beckenstein, Edward (2011). Topological Vector Spaces. Pure and applied mathematics (Second ed.). Boca Raton, FL: CRC Press. ISBN 978-1584888666. OCLC 144216834.
- Robertson, Alex P.; Robertson, Wendy J. (1980). Topological Vector Spaces. Cambridge Tracts in Mathematics. 53. Cambridge England: Cambridge University Press. ISBN 978-0-521-29882-7. OCLC 589250.
- Rudin, Walter (1991). Functional Analysis. International Series in Pure and Applied Mathematics. 8 (Second ed.). New York, NY: McGraw-Hill Science/Engineering/Math. ISBN 978-0-07-054236-5. OCLC 21163277.
- Schaefer, Helmut H.; Wolff, Manfred P. (1999). Topological Vector Spaces. GTM. 8 (Second ed.). New York, NY: Springer New York Imprint Springer. ISBN 978-1-4612-7155-0. OCLC 840278135.
- Swartz, Charles (1992). An introduction to Functional Analysis. New York: M. Dekker. ISBN 978-0-8247-8643-4. OCLC 24909067.
- Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322.
- Wilansky, Albert (2013). Modern Methods in Topological Vector Spaces. Mineola, New York: Dover Publications, Inc. ISBN 978-0-486-49353-4. OCLC 849801114.