# Convex combination

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In convex geometry, a convex combination is a linear combination of points (which can be vectors, scalars, or more generally points in an affine space) where all coefficients are non-negative and sum to 1. [1]

## Contents

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

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

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

As a particular example, every convex combination of two points lies on the line segment between the points. [1]

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. [1]

There exist subsets of a vector space that are not closed under linear combinations but are closed under convex combinations. For example, the interval ${\displaystyle [0,1]}$ 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

• Similarly, a convex combination ${\displaystyle X}$ of random variables ${\displaystyle Y_{i}}$ is a weighted sum (where ${\displaystyle \alpha _{i}}$ satisfy the same constraints as above) of its component probability distributions, often called a finite mixture distribution, with probability density function:
${\displaystyle f_{X}(x)=\sum _{i=1}^{n}\alpha _{i}f_{Y_{i}}(x)}$
• A conical combination is a linear combination with nonnegative coefficients. When a point ${\displaystyle x}$ is to be used as the reference origin for defining displacement vectors, then ${\displaystyle x}$ is a convex combination of ${\displaystyle n}$ points ${\displaystyle x_{1},x_{2},\dots ,x_{n}}$ if and only if the zero displacement is a non-trivial conical combination of their ${\displaystyle n}$ respective displacement vectors relative to ${\displaystyle x}$.
• 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 count 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.

## Related Research Articles

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In mathematics, an affine combination of x1, ..., xn is a linear combination

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Given a finite number of vectors in a real vector space, a conical combination, conical sum, or weighted sum of these vectors is a vector of the form

In algebra, a multivariate polynomial

This is a glossary for the terminology applied in the mathematical theories of Lie groups and Lie algebras. For the topics in the representation theory of Lie groups and Lie algebras, see Glossary of representation theory. Because of the lack of other options, the glossary also includes some generalizations such as quantum group.

## References

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