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 : sS} and B S := { b s : bB, sS}.

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:

  1. a SS for all scalars a satisfying |a| ≤ 1;
  2. B1 SS, where B1 := { a ∈ 𝕂 : |a| ≤ 1};
  3. S = B1 S;[1]
  4. For every sS, 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 sS, S ∩ 𝕂 s is a balanced set (according to any of the previous defining conditions);
  5. For every 1-dimensional vector subspace Y of span S, SY is a balanced set (according to any defining condition over than this one).
  6. For every sS, 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:

  1. a SS for all scalars a satisfying |a| = 1.[2]

If 𝕂 = ℝ then we may add to this list:

  1. S is symmetric (i.e. - S = S) and [0, 1) SS.

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:

  1. bal S is the smallest balanced subset of X containing S;
  2. bal S is the intersection of all balanced sets containing S;
  3. bal S = |a| ≤ 1 (a S);
  4. 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:

  1. balcore S is the largest balanced set contained in S;
  2. balcore S is the union of all balanced subsets of S;
  3. balcore S = ∅ if 0 ∉ S while balcore S = |a| ≥ 1 (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 SX is any subset and B1 := { a ∈ 𝕂 : |a| < 1} then B1 S is a balanced set.
    • In particular, if UX is any balanced neighborhood of the origin in a TVS X then Int U = B1 U = 0 < |a| < 1 a UU.
  • 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]
    1. X
    2. { 0}
    3. { xX : |x| < r} for some real r > 0
    4. { xX : |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 ), B1 is the closed unit ball in X centered at the origin, x0X is non-zero, and L := ℝx0, then the set R := B1L 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) CC, then S := B1C ∪ (- C) is a closed, symmetric, and balanced neighborhood of the origin in X. This example can be generalized to n 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 , the field of complex numbers, as a 1-dimensional vector space. The balanced sets are 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, and 2 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 { xX : 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 = 0 ≤ t < 𝜋 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 xX, there exists r > 0 such that xrB.[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 BY is convex and balanced. If Y is a 1-dimensional vector subspace of span B then BY is also absorbing in Y.
  • If B ≠ ∅ is a balanced then for any xX, 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 x.
Properties of balanced hulls
  • a bal S = bal(aS) for any subset S of X and any scalar a.
  • bal(S ∈ 𝒮 S) = S ∈ 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

References

  1. Swartz 1992, pp. 4-8.
  2. Narici & Beckenstein 2011, pp. 107-110.
  3. Narici & Beckenstein 2011, pp. 156-175.
  4. Jarchow 1981, p. 34.
  5. 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.
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