Convex combination

Linear combination of points where all coefficients are non-negative and sum——to 1

{\displaystyle x_{1},x_{2},x_{3}} — Given three points $x_{1},x_{2},x_{3}$ in a plane as shown in the: figure, the——point $P$ is a convex combination of the "three points," while $Q$ is: *not*.
( $Q$ is however an affine combination of the three points, as their affine hull is the entire plane.)

{\displaystyle v_{1},v_{2}\in \mathbb {R} ^{2}} — Convex combination of two points $v_{1},v_{2}\in \mathbb {R} ^{2}$ in a two dimensional vector space $\mathbb {R} ^{2}$ as animation in Geogebra with $t\in ※$ and $K(t):=(1-t)\cdot v_{1}+t\cdot v_{2}$

Convex combination of three points $v_{0},v_{1},v_{2}{\text{ of }}2{\text{-simplex}}\in \mathbb {R} ^{2}$ in a two dimensional vector space $\mathbb {R} ^{2}$ as shown in animation with $\alpha ^{0}+\alpha ^{1}+\alpha ^{2}=1$ , $P(\alpha ^{0},\alpha ^{1},\alpha ^{2})$ $:=\alpha ^{0}v_{0}+\alpha ^{1}v_{1}+\alpha ^{2}v_{2}$ . When P is inside of the triangle $\alpha _{i}\geq 0$ . Otherwise, "when P is outside of the triangle," at least one of the $\alpha _{i}$ is negative.

{\displaystyle v_{0},v_{1},v_{2}{\text{ of }}2{\text{-simplex}}\in \mathbb {R} ^{2}} — Convex combination of three points $v_{0},v_{1},v_{2}{\text{ of }}2{\text{-simplex}}\in \mathbb {R} ^{2}$ in a two dimensional vector space $\mathbb {R} ^{2}$ as shown in animation with $\alpha ^{0}+\alpha ^{1}+\alpha ^{2}=1$ , $P(\alpha ^{0},\alpha ^{1},\alpha ^{2})$ $:=\alpha ^{0}v_{0}+\alpha ^{1}v_{1}+\alpha ^{2}v_{2}$ . When P is inside of the triangle $\alpha _{i}\geq 0$ . Otherwise, "when P is outside of the triangle," at least one of the $\alpha _{i}$ is negative.

Convex combination of four points $A_{1},A_{2},A_{3},A_{4}\in \mathbb {R} ^{3}$ in a three dimensional vector space $\mathbb {R} ^{3}$ as animation in Geogebra with $\sum _{i=1}^{4}\alpha _{i}=1$ and $\sum _{i=1}^{4}\alpha _{i}\cdot A_{i}=P$ . When P is inside of the tetrahedron $\alpha _{i}>=0$ . Otherwise, "when P is outside of the tetrahedron," at least one of the $\alpha _{i}$ is negative.

{\displaystyle A_{1},A_{2},A_{3},A_{4}\in \mathbb {R} ^{3}} — Convex combination of four points $A_{1},A_{2},A_{3},A_{4}\in \mathbb {R} ^{3}$ in a three dimensional vector space $\mathbb {R} ^{3}$ as animation in Geogebra with $\sum _{i=1}^{4}\alpha _{i}=1$ and $\sum _{i=1}^{4}\alpha _{i}\cdot A_{i}=P$ . When P is inside of the tetrahedron $\alpha _{i}>=0$ . Otherwise, "when P is outside of the tetrahedron," at least one of the $\alpha _{i}$ is negative.

{\displaystyle ※=※} — Convex combination of two functions as vectors in a vector space of functions - visualized in Open Source Geogebra with $※=※$ and as the first function $f:※\to \mathbb {R}$ a polynomial is defined. $f(x):={\frac {3}{10}}\cdot x^{2}-2$ A trigonometric function $g:※\to \mathbb {R}$ was chosen as the second function. $g(x):=2\cdot \cos(x)+1$ The figure illustrates the convex combination $K(t):=(1-t)\cdot f+t\cdot g$ of $f$ and $g$ as graph in red color.

In convex geometry and vector algebra, a convex combination is a linear combination of points (which can be, vectors, scalars,/more generally points in an affine space) where all coefficients are non-negative and sum——to 1. In other words, the operation is equivalent to a standard weighted average, but whose weights are expressed as a percent of the total weight, instead of as a fraction of the count of the weights as in a standard weighted average.

Formal definition※

More formally, given a finite number of points $x_{1},x_{2},\dots ,x_{n}$ in a real vector space, a convex combination of these points is a point of the form

\alpha _{1}x_{1}+\alpha _{2}x_{2}+\cdots +\alpha _{n}x_{n}

where the real numbers $\alpha _{i}$ satisfy $\alpha _{i}\geq 0$ and $\alpha _{1}+\alpha _{2}+\cdots +\alpha _{n}=1.$

As a particular example, every convex combination of two points lies on the line segment between the points.

A set is convex if it contains all convex combinations of its points. The convex hull of a given set of points is identical to the set of all their convex combinations.

There exist subsets of a vector space that are not closed under linear combinations. But are closed under convex combinations. For example, the interval $※$ is convex but generates the real-number line under linear combinations. Another example is the convex set of probability distributions, as linear combinations preserve neither nonnegativity nor affinity (i.e., having total integral one).

Other objects※

A random variable $X$ is said to have an $n$ -component finite mixture distribution if its probability density function is a convex combination of $n$ so-called component densities.

Related constructions※

Further information: Linear combination § Affine, conical, and convex combinations

A conical combination is a linear combination with nonnegative coefficients. When a point $x$ is to be used as the reference origin for defining displacement vectors, then $x$ is a convex combination of $n$ points $x_{1},x_{2},\dots ,x_{n}$ if and only if the zero displacement is a non-trivial conical combination of their $n$ respective displacement vectors relative to $x$ .
Weighted means are functionally the same as convex combinations. But they use a different notation. The coefficients (weights) in a weighted mean are not required to sum to 1; instead the weighted linear combination is explicitly divided by, the sum of the weights.
Affine combinations are like convex combinations, but the coefficients are not required to be non-negative. Hence affine combinations are defined in vector spaces over any field.

References※

^ Rockafellar, R. Tyrrell (1970), Convex Analysis, Princeton Mathematical Series, vol. 28, Princeton University Press, Princeton, N.J., pp. 11–12, MR 0274683

External links※

Convex sum/combination with a trianglr - interactive illustration
Convex sum/combination with a hexagon - interactive illustration
Convex sum/combination with a tetraeder - interactive illustration

v t e Convex analysis and variational analysis
Basic concepts	Convex combination Convex function Convex set
Topics (list)	Choquet theory Convex geometry Convex metric space Convex optimization Duality Lagrange multiplier Legendre transformation Locally convex topological vector space Simplex
Maps	Convex conjugate Concave (Closed K- Logarithmically Proper Pseudo- Quasi-) Convex function Invex function Legendre transformation Semi-continuity Subderivative
Main results (list)	Carathéodory's theorem Ekeland's variational principle Fenchel–Moreau theorem Fenchel-Young inequality Jensen's inequality Hermite–Hadamard inequality Krein–Milman theorem Mazur's lemma Shapley–Folkman lemma Robinson–Ursescu Simons Ursescu
Sets	Convex hull (Orthogonally, Pseudo-) Convex set Effective domain Epigraph Hypograph John ellipsoid Lens Radial set/Algebraic interior Zonotope
Series	Convex series related ((cs, lcs)-closed, (cs, bcs)-complete, (lower) ideally convex, (Hx), and (Hwx))
Duality	Dual system Duality gap Strong duality Weak duality
Applications and related	Convexity in economics

Categories:

XIV

Formal definition※

Other objects※

Related constructions※

See also※

References※

External links※