Convex set explained

In geometry, a subset of a Euclidean space, or more generally an affine space over the reals, is convex if, given any two points in the subset, the subset contains the whole line segment that joins them. Equivalently, a convex set or a convex region is a subset that intersects every line into a single line segment (possibly empty).^{[1] [2]} For example, a solid cube is a convex set, but anything that is hollow or has an indent, for example, a crescent shape, is not convex.

The boundary of a convex set in the plane is always a convex curve. The intersection of all the convex sets that contain a given subset of Euclidean space is called the convex hull of . It is the smallest convex set containing .

A convex function is a real-valued function defined on an interval with the property that its epigraph (the set of points on or above the graph of the function) is a convex set. Convex minimization is a subfield of optimization that studies the problem of minimizing convex functions over convex sets. The branch of mathematics devoted to the study of properties of convex sets and convex functions is called convex analysis.

The notion of a convex set can be generalized as described below.

Definitions

Let be a vector space or an affine space over the real numbers, or, more generally, over some ordered field (this includes Euclidean spaces, which are affine spaces). A subset of is convex if, for all and in, the line segment connecting and is included in .

This means that the affine combination belongs to for all in and in the interval . This implies that convexity is invariant under affine transformations. Further, it implies that a convex set in a real or complex topological vector space is path-connected (and therefore also connected).

A set is if every point on the line segment connecting and other than the endpoints is inside the topological interior of . A closed convex subset is strictly convex if and only if every one of its boundary points is an extreme point.

A set is absolutely convex if it is convex and balanced.

Examples

The convex subsets of (the set of real numbers) are the intervals and the points of . Some examples of convex subsets of the Euclidean plane are solid regular polygons, solid triangles, and intersections of solid triangles. Some examples of convex subsets of a Euclidean 3-dimensional space are the Archimedean solids and the Platonic solids. The Kepler-Poinsot polyhedra are examples of non-convex sets.

Non-convex set

A set that is not convex is called a non-convex set. A polygon that is not a convex polygon is sometimes called a concave polygon,^[3] and some sources more generally use the term concave set to mean a non-convex set, but most authorities prohibit this usage.^{[4] [5]}

The complement of a convex set, such as the epigraph of a concave function, is sometimes called a reverse convex set, especially in the context of mathematical optimization.^[6]

Properties

Given points in a convex set, and nonnegative numbers such that, the affine combination $$\backslash sum\_^r\backslash lambda\_k\; u\_k$$belongs to . As the definition of a convex set is the case, this property characterizes convex sets.

Such an affine combination is called a convex combination of .

Intersections and unions

The collection of convex subsets of a vector space, an affine space, or a Euclidean space has the following properties:^{[7] [8]}

1. The empty set and the whole space are convex.
2. The intersection of any collection of convex sets is convex.
3. The union of a sequence of convex sets is convex, if they form a non-decreasing chain for inclusion. For this property, the restriction to chains is important, as the union of two convex sets need not be convex.

Closed convex sets

Closed convex sets are convex sets that contain all their limit points. They can be characterised as the intersections of closed half-spaces (sets of points in space that lie on and to one side of a hyperplane).

From what has just been said, it is clear that such intersections are convex, and they will also be closed sets. To prove the converse, i.e., every closed convex set may be represented as such intersection, one needs the supporting hyperplane theorem in the form that for a given closed convex set and point outside it, there is a closed half-space that contains and not . The supporting hyperplane theorem is a special case of the Hahn–Banach theorem of functional analysis.

Convex sets and rectangles

Let be a convex body in the plane (a convex set whose interior is non-empty). We can inscribe a rectangle r in such that a homothetic copy R of r is circumscribed about . The positive homothety ratio is at most 2 and:^[9] $$\backslash tfrac\; \backslash cdot\backslash operatorname(R)\; \backslash leq\; \backslash operatorname(C)\; \backslash leq\; 2\backslash cdot\; \backslash operatorname(r)$$

Blaschke-Santaló diagrams

The set

l{K} ^{2} of all planar convex bodies can be parameterized in terms of the convex body diameter D, its inradius r (the biggest circle contained in the convex body) and its circumradius R (the smallest circle containing the convex body). In fact, this set can be described by the set of inequalities given by^{[10] [11]} $$2r\; \backslash le\; D\; \backslash le\; 2R$$$$R\; \backslash le\; \backslash frac\; D$$$$r\; +\; R\; \backslash le\; D$$$$D^2\; \backslash sqrt\; \backslash le\; 2R\; (2R\; +\; \backslash sqrt)$$and can be visualized as the image of the function g that maps a convex body to the point given by (r/R, D/2R). The image of this function is known a (r, D, R) Blachke-Santaló diagram.Alternatively, the set l{K} ^{2} can also be parametrized by its width (the smallest distance between any two different parallel support hyperplanes), perimeter and area.

Other properties

Let X be a topological vector space and

C \subseteq X be convex. \operatorname{Cl} C and \operatorname{Int} C are both convex (i.e. the closure and interior of convex sets are convex).

• If

a \in \operatorname{Int} C and b \in \operatorname{Cl} C then [a, b[ \subseteq \operatorname{Int} C (where [a, b[ : = \left\{ ( 1 - r) a + r b : 0 \leq r < 1 \right\} ).

• If

\operatorname{Int} C ≠ \emptyset then:\operatorname{cl} \left( \operatorname{Int} C \right) = \operatorname{Cl} C , and\operatorname{Int} C = \operatorname{Int} \left( \operatorname{Cl} C \right) = C ^{i} , where C ^{i} is the algebraic interior of C.

Convex hulls and Minkowski sums

Convex hulls

See main article: convex hull. Every subset of the vector space is contained within a smallest convex set (called the convex hull of), namely the intersection of all convex sets containing . The convex-hull operator Conv has the characteristic properties of a hull operator:

• extensive:,
• non-decreasing: implies that, and
• idempotent: .

The convex-hull operation is needed for the set of convex sets to form a lattice, in which the "join" operation is the convex hull of the union of two convex sets$$\backslash operatorname(S)\backslash vee\backslash operatorname(T)\; =\; \backslash operatorname(S\backslash cup\; T)\; =\; \backslash operatorname\backslash bigl(\backslash operatorname(S)\backslash cup\backslash operatorname(T)\backslash bigr).$$The intersection of any collection of convex sets is itself convex, so the convex subsets of a (real or complex) vector space form a complete lattice.

Minkowski addition

See main article: Minkowski addition.

In a real vector-space, the Minkowski sum of two (non-empty) sets, and, is defined to be the set formed by the addition of vectors element-wise from the summand-sets$$S\_1+S\_2=\backslash .$$More generally, the Minkowski sum of a finite family of (non-empty) sets is the set formed by element-wise addition of vectors$$\backslash sum\_n\; S\_n\; =\; \backslash left\; \backslash .$$

For Minkowski addition, the zero set  containing only the zero vector  has special importance: For every non-empty subset S of a vector space$$S+\backslash =S;$$in algebraic terminology, is the identity element of Minkowski addition (on the collection of non-empty sets).^[12]

Convex hulls of Minkowski sums

Minkowski addition behaves well with respect to the operation of taking convex hulls, as shown by the following proposition:

Let be subsets of a real vector-space, the convex hull of their Minkowski sum is the Minkowski sum of their convex hulls$$\backslash operatorname(S\_1+S\_2)=\backslash operatorname(S\_1)+\backslash operatorname(S\_2).$$

This result holds more generally for each finite collection of non-empty sets:$$\backslash text\backslash left\; (\backslash sum\_n\; S\_n\; \backslash right)\; =\; \backslash sum\_n\; \backslash text\; \backslash left\; (S\_n\; \backslash right).$$

In mathematical terminology, the operations of Minkowski summation and of forming convex hulls are commuting operations.^{[13] [14]}

Minkowski sums of convex sets

The Minkowski sum of two compact convex sets is compact. The sum of a compact convex set and a closed convex set is closed.^[15]

The following famous theorem, proved by Dieudonné in 1966, gives a sufficient condition for the difference of two closed convex subsets to be closed.^[16] It uses the concept of a recession cone of a non-empty convex subset S, defined as:$$\backslash operatorname\; S\; =\; \backslash left\backslash ,$$where this set is a convex cone containing

0 \in X and satisfying S + \operatorname{rec} S = S . Note that if S is closed and convex then \operatorname{rec} S is closed and for all s _{0} \in S ,$$\backslash operatorname\; S\; =\; \backslash bigcap\_\; t\; (S\; -\; s\_0).$$

Theorem (Dieudonné). Let A and B be non-empty, closed, and convex subsets of a locally convex topological vector space such that

\operatorname{rec} A \cap \operatorname{rec} B is a linear subspace. If A or B is locally compact then A − B is closed.

Generalizations and extensions for convexity

The notion of convexity in the Euclidean space may be generalized by modifying the definition in some or other aspects. The common name "generalized convexity" is used, because the resulting objects retain certain properties of convex sets.

Star-convex (star-shaped) sets

See main article: Star domain. Let be a set in a real or complex vector space. is star convex (star-shaped) if there exists an in such that the line segment from to any point in is contained in . Hence a non-empty convex set is always star-convex but a star-convex set is not always convex.

Orthogonal convexity

See main article: Orthogonal convex hull. An example of generalized convexity is orthogonal convexity.^[17]

A set in the Euclidean space is called orthogonally convex or ortho-convex, if any segment parallel to any of the coordinate axes connecting two points of lies totally within . It is easy to prove that an intersection of any collection of orthoconvex sets is orthoconvex. Some other properties of convex sets are valid as well.

Non-Euclidean geometry

The definition of a convex set and a convex hull extends naturally to geometries which are not Euclidean by defining a geodesically convex set to be one that contains the geodesics joining any two points in the set.

Order topology

Convexity can be extended for a totally ordered set endowed with the order topology.^[18]

Let . The subspace is a convex set if for each pair of points in such that, the interval is contained in . That is, is convex if and only if for all in, implies .

A convex set is connected in general: a counter-example is given by the subspace in, which is both convex and not connected.

Convexity spaces

The notion of convexity may be generalised to other objects, if certain properties of convexity are selected as axioms.

Given a set, a convexity over is a collection of subsets of satisfying the following axioms:^[19]

1. The empty set and are in
2. The intersection of any collection from is in .
3. The union of a chain (with respect to the inclusion relation) of elements of is in .

The elements of are called convex sets and the pair is called a convexity space. For the ordinary convexity, the first two axioms hold, and the third one is trivial.

For an alternative definition of abstract convexity, more suited to discrete geometry, see the convex geometries associated with antimatroids.

Convex spaces

See main article: Convex space.

Convexity can be generalised as an abstract algebraic structure: a space is convex if it is possible to take convex combinations of points.

See also

• Absorbing set
• Algorithmic problems on convex sets
• Bounded set (topological vector space)
• Brouwer fixed-point theorem
• Complex convexity
• Convex hull
• Convex series
• Convex metric space
• Carathéodory's theorem (convex hull)
• Choquet theory
• Helly's theorem
• Holomorphically convex hull
• Integrally-convex set
• John ellipsoid
• Pseudoconvexity
• Radon's theorem
• Shapley–Folkman lemma
• Symmetric set

External links

• • Lectures on Convex Sets, notes by Niels Lauritzen, at Aarhus University, March 2010.

Notes and References

1. Book: Morris. Carla C.. Stark. Robert M.. Finite Mathematics: Models and Applications. 24 August 2015. John Wiley & Sons. 9781119015383. 121. 5 April 2017. en.
2. Kjeldsen. Tinne Hoff. History of Convexity and Mathematical Programming. Proceedings of the International Congress of Mathematicians. ICM 2010. 3233–3257. 10.1142/9789814324359_0187. 5 April 2017. dead. https://web.archive.org/web/20170811100026/http://www.mathunion.org/ICM/ICM2010.4/Main/icm2010.4.3233.3257.pdf. 2017-08-11.
3. Book: McConnell, Jeffrey J. . 2006 . Computer Graphics: Theory Into Practice . 0-7637-2250-2 . 130 . .
4. Book: Takayama, Akira. Analytical Methods in Economics. University of Michigan Press. 1994. 9780472081356. 54. An often seen confusion is a "concave set". Concave and convex functions designate certain classes of functions, not of sets, whereas a convex set designates a certain class of sets, and not a class of functions. A "concave set" confuses sets with functions..
5. Book: An Introduction to Mathematical Analysis for Economic Theory and Econometrics. Dean. Corbae. Maxwell B.. Stinchcombe. Juraj. Zeman. Princeton University Press. 2009. 9781400833085. 347. There is no such thing as a concave set..
6. Meyer . Robert . SIAM Journal on Control and Optimization . 0312915 . 41–54 . The validity of a family of optimization methods . 8 . 1970. 10.1137/0308003 . .
7. Soltan, Valeriu, Introduction to the Axiomatic Theory of Convexity, Ştiinţa, Chişinău, 1984 (in Russian).
8. Book: Singer, Ivan. Abstract convex analysis. Canadian Mathematical Society series of monographs and advanced texts. John Wiley &&nbsp;Sons, Inc.. New York. 1997. xxii+491. 0-471-16015-6. 1461544.
9. 10.1007/BF01263495. Approximation of convex bodies by rectangles. Geometriae Dedicata. 47. 111–117. 1993. Lassak . M. . 119508642.
10. Santaló. L.. 1961. Sobre los sistemas completos de desigualdades entre tres elementos de una figura convexa planas. Mathematicae Notae. 17. 82–104.
11. Brandenberg. René. González Merino. Bernardo. 2017. A complete 3-dimensional Blaschke-Santaló diagram. Mathematical Inequalities & Applications. en. 2. 301–348. 10.7153/mia-20-22. 1331-4343. free. 1404.6808.
12. The empty set is important in Minkowski addition, because the empty set annihilates every other subset: For every subset of a vector space, its sum with the empty set is empty: S + \emptyset = \emptyset .
13. Theorem 3 (pages 562–563): M.. Krein. Mark Krein. V.. Šmulian. 1940. On regularly convex sets in the space conjugate to a Banach space. Annals of Mathematics . Second Series. 41 . 3 . 556–583. 1968735. 10.2307/1968735.
14. For the commutativity of Minkowski addition and convexification, see Theorem 1.1.2 (pages 2–3) in Schneider; this reference discusses much of the literature on the convex hulls of Minkowski sumsets in its "Chapter 3 Minkowski addition" (pages 126–196): Book: Schneider, Rolf. Convex bodies: The Brunn–Minkowski theory. Encyclopedia of mathematics and its applications. 44. Cambridge University Press. Cambridge. 1993. xiv+490. 0-521-35220-7. 1216521.
15. Lemma 5.3: Book: C.D.. Aliprantis. K.C.. Border. Infinite Dimensional Analysis, A Hitchhiker's Guide. Springer. Berlin. 2006. 978-3-540-29587-7.
16. Book: Zălinescu, C.. Convex analysis in general vector spaces. limited. World Scientific Publishing Co., Inc. River Edge, NJ . 2002. 7. 981-238-067-1. 1921556.
17. Rawlins G.J.E. and Wood D, "Ortho-convexity and its generalizations", in: Computational Morphology, 137-152. Elsevier, 1988.
18. [James Munkres|Munkres, James]
19. Book: van De Vel, Marcel L. J.. Theory of convex structures. North-Holland Mathematical Library. North-Holland Publishing Co.. Amsterdam. 1993. xvi+540. 0-444-81505-8. 1234493.