Illustration of a convex set which looks somewhat like a deformed circle. The (black) line segment joining points x and y lies completely within the (green) set. Since this is true for any points x and y within the set that we might choose, the set is convex.
Illustration of a nonconvex set. Since the red part of the (black and red) linesegment joining the points x and y lies outside of the (green) set, the set is nonconvex.
In Euclidean space, a convex set is the region such that, for every pair of points within the region, every point on the straight line segment that joins the pair of points is also within the region. For example, a solid cube is a convex set, but anything that is hollow or has a dent in it, for example, a crescent shape, is not convex. A convex curve forms the boundary of a convex set.
The notion of a convex set can be generalized to other spaces as described below.
In vector spaces
A
function is convex if and only if its
epigraph, the region (in green) above its
graph (in blue), is a convex set.
Let S be a vector space over the real numbers, or, more generally, some ordered field. This includes Euclidean spaces. A set C in S is said to be convex if, for all x and y in C and all t in the interval [0, 1], the point (1 − t)x + ty also belongs to C. In other words, every point on the line segment connecting x and y is in C. This implies that a convex set in a real or complex topological vector space is pathconnected, thus connected. Furthermore, C is strictly convex if every point on the line segment connecting x and y other than the endpoints is inside the interior of C.
A set C is called absolutely convex if it is convex and balanced.
The convex subsets of R (the set of real numbers) are simply the intervals of R. 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 3dimensional space are the Archimedean solids and the Platonic solids. The KeplerPoinsot polyhedra are examples of nonconvex sets.
Nonconvex set

"Concave set" redirects here.
A set that is not convex is called a nonconvex set. A polygon that is not a convex polygon is sometimes called a concave polygon,^{[1]} and some sources more generally use the term concave set to mean a nonconvex set,^{[2]} but most authorities prohibit this usage.^{[3]}^{[4]}
Properties
If S is a convex set in ndimensional space, then for any collection of r, r > 1, ndimensional vectors u_{1}, ..., u_{r} in S, and for any nonnegative numbers λ_{1}, ..., λ_{r} such that λ_{1} + ... + λ_{r} = 1, then one has:

\sum_{k=1}^r\lambda_k u_k \in S.
A vector of this type is known as a convex combination of u_{1}, ..., u_{r}.
Intersections and unions
The collection of convex subsets of a vector space has the following properties:^{[5]}^{[6]}

The empty set and the whole vectorspace are convex.

The intersection of any collection of convex sets is convex.

The union of a nondecreasing sequence of convex subsets is a convex set. For the preceding property of unions of nondecreasing sequences of convex sets, the restriction to nested sets is important: 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 halfspaces (sets of point 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 convex set may be represented as such intersection, one needs the supporting hyperplane theorem in the form that for a given closed convex set C and point P outside it, there is a closed halfspace H that contains C and not P. The supporting hyperplane theorem is a special case of the Hahn–Banach theorem of functional analysis.
Convex sets and rectangles
Let C be a convex body in the plane. We can inscribe a rectangle r in C such that a homothetic copy R of r is circumscribed about C. The positive homothety ratio is at most 2 and:^{[7]}


\tfrac{1}{2} \cdot\text{Area}(R) \leq \text{Area}(C) \leq 2\cdot \text{Area}(r)
Convex hulls and Minkowski sums
Convex hulls
Every subset A of the vector space is contained within a smallest convex set (called the convex hull of A), namely the intersection of all convex sets containing A. The convexhull operator Conv() has the characteristic properties of a hull operator:

The convexhull 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

Conv(S) ∨ Conv(T) = Conv(S ∪ T) = Conv(Conv(S) ∪ Conv(T)).
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
Minkowski addition of sets. The
sum of the squares Q
_{1}=[0,1]
^{2} and Q
_{2}=[1,2]
^{2} is the square Q
_{1}+Q
_{2}=[1,3]
^{2}.
Three squares are shown in the nonnegative quadrant of the Cartesian plane. The square Q_{1} = [0, 1] × [0, 1] is green. The square }.
In a real vectorspace, the Minkowski sum of two (nonempty) sets, S_{1} and S_{2}, is defined to be the set S_{1} + S_{2} formed by the addition of vectors elementwise from the summandsets

S_{1} + S_{2} = {x_{1} + x_{2} : x_{1} ∈ S_{1}, x_{2} ∈ S_{2}} .
More generally, the Minkowski sum of a finite family of (nonempty) sets S_{n} is the set formed by elementwise addition of vectors

\sum_n S_n = \left \{ \sum_n x_n : x_n \in S_n \right \}.
For Minkowski addition, the zero set {0} containing only the zero vector 0 has special importance: For every nonempty subset S of a vector space

S + {0} = S;
in algebraic terminology, {0} is the identity element of Minkowski addition (on the collection of nonempty sets).^{[8]}
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 S_{1}, S_{2} be subsets of a real vectorspace, the convex hull of their Minkowski sum is the Minkowski sum of their convex hulls

Conv(S_{1} + S_{2}) = Conv(S_{1}) + Conv(S_{2}).
This result holds more generally for each finite collection of nonempty sets:

\text{Conv}\left ( \sum_n S_n \right ) = \sum_n \text{Conv} \left (S_n \right).
In mathematical terminology, the operations of Minkowski summation and of forming convex hulls are commuting operations.^{[9]}^{[10]}
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.^{[11]}
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.
Starconvex sets
Let C be a set in a real or complex vector space. C is star convex if there exists an x_{0} in C such that the line segment from x_{0} to any point y in C is contained in C. Hence a nonempty convex set is always starconvex but a starconvex set is not always convex.
Orthogonal convexity
An example of generalized convexity is orthogonal convexity.^{[12]}
A set S in the Euclidean space is called orthogonally convex or orthoconvex, if any segment parallel to any of the coordinate axes connecting two points of S lies totally within S. 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.
NonEuclidean 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 space X endowed with the order topology, using the total order < of the space.^{[13]}
Let Y ⊆ X. The subspace Y is a convex set if for each pair of points a, b in Y such that a < b, the interval (a, b) = {x ∈ X : a < x < b} is contained in Y. That is, Y is convex if and only if for all a, b in Y, a < b implies (a, b) ⊆ Y.
Convexity spaces
The notion of convexity may be generalised to other objects, if certain properties of convexity are selected as axioms.
Given a set X, a convexity over X is a collection 𝒞 of subsets of X satisfying the following axioms:^{[14]}^{[5]}^{[6]}

The empty set and X are in 𝒞

The intersection of any collection from 𝒞 is in 𝒞.

The union of a chain (with respect to the inclusion relation) of elements of 𝒞 is in 𝒞.
The elements of 𝒞 are called convex sets and the pair (X, 𝒞) 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.
See also
References

^ .

^ Weisstein, Eric W., "Concave", MathWorld.

^

^

^ ^{a} ^{b} Soltan, Valeriu, Introduction to the Axiomatic Theory of Convexity, Ştiinţa, Chişinău, 1984 (in Russian).

^ ^{a} ^{b}

^

^ The empty set is important in Minkowski addition, because the empty set annihilates every other subset: For every subset S of a vector space, its sum with the empty set is empty: S + ∅ = ∅.

^ Theorem 3 (pages 562–563):

^ 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):

^ Lemma 5.3:

^ Rawlins G.J.E. and Wood D, "Orthoconvexity and its generalizations", in: Computational Morphology, 137152. Elsevier, 1988.

^ Munkres, James; Topology, Prentice Hall; 2nd edition (December 28, 1999). ISBN 0131816292.

^
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