# Majorization

In mathematics, majorization is a preorder on vectors of real numbers. For a vector ${\displaystyle \mathbf {a} \in \mathbb {R} ^{d}}$ , we denote by ${\displaystyle \mathbf {a} ^{\downarrow }\in \mathbb {R} ^{d}}$ the vector with the same components, but sorted in descending order. Given ${\displaystyle \mathbf {a} ,\mathbf {b} \in \mathbb {R} ^{d}}$ , we say that ${\displaystyle \mathbf {a} }$ weakly majorizes (or dominates) ${\displaystyle \mathbf {b} }$ from below written as ${\displaystyle \mathbf {a} \succ _{w}\mathbf {b} }$ iff

${\displaystyle \sum _{i=1}^{k}a_{i}^{\downarrow }\geq \sum _{i=1}^{k}b_{i}^{\downarrow }\quad {\text{for }}k=1,\dots ,d,}$

Equivalently, we say that ${\displaystyle \mathbf {b} }$ is weakly majorized (or dominated) by ${\displaystyle \mathbf {a} }$ from below, written as ${\displaystyle \mathbf {b} \prec _{w}\mathbf {a} }$ .

If ${\displaystyle \mathbf {a} \succ _{w}\mathbf {b} }$ and in addition ${\displaystyle \sum _{i=1}^{d}a_{i}=\sum _{i=1}^{d}b_{i}}$ , we say that ${\displaystyle \mathbf {a} }$ majorizes (or dominates) ${\displaystyle \mathbf {b} }$ , written as ${\displaystyle \mathbf {a} \succ \mathbf {b} }$ . Equivalently, we say that ${\displaystyle \mathbf {b} }$ is majorized (or dominated) by ${\displaystyle \mathbf {a} }$ , written as ${\displaystyle \mathbf {b} \prec \mathbf {a} }$ .

Note that the majorization order does not depend on the order of the components of the vectors ${\displaystyle \mathbf {a} }$ or ${\displaystyle \mathbf {b} }$ . Majorization is not a partial order, since ${\displaystyle \mathbf {a} \succ \mathbf {b} }$ and ${\displaystyle \mathbf {b} \succ \mathbf {a} }$ do not imply ${\displaystyle \mathbf {a} =\mathbf {b} }$ , it only implies that the components of each vector are equal, but not necessarily in the same order.

Note that the notation is inconsistent in the mathematical literature: some use the reverse notation, e.g., ${\displaystyle \succ }$ is replaced with ${\displaystyle \prec }$ .

A function ${\displaystyle f:\mathbb {R} ^{d}\to \mathbb {R} }$ is said to be Schur convex when ${\displaystyle \mathbf {a} \succ \mathbf {b} }$ implies ${\displaystyle f(\mathbf {a} )\geq f(\mathbf {b} )}$ . Similarly, ${\displaystyle f(\mathbf {a} )}$ is Schur concave when ${\displaystyle \mathbf {a} \succ \mathbf {b} }$ implies ${\displaystyle f(\mathbf {a} )\leq f(\mathbf {b} ).}$

The majorization partial order on finite sets, described here, can be generalized to the Lorenz ordering, a partial order on distribution functions. For example, a wealth distribution is Lorenz-greater than another iff its Lorenz curve lies below the other. As such, a Lorenz-greater wealth distribution has a higher Gini coefficient, and has more income inequality.

## Examples

The order of the entries does not affect the majorization, e.g., the statement ${\displaystyle (1,2)\prec (0,3)}$ is simply equivalent to ${\displaystyle (2,1)\prec (3,0)}$ .

(Strong) majorization: ${\displaystyle (1,2,3)\prec (0,3,3)\prec (0,0,6)}$ . For vectors with n components

${\displaystyle \left({\frac {1}{n}},\ldots ,{\frac {1}{n}}\right)\prec \left({\frac {1}{n-1}},\ldots ,{\frac {1}{n-1}},0\right)\prec \cdots \prec \left({\frac {1}{2}},{\frac {1}{2}},0,\ldots ,0\right)\prec \left(1,0,\ldots ,0\right).}$

(Weak) majorization: ${\displaystyle (1,2,3)\prec _{w}(1,3,3)\prec _{w}(1,3,4)}$ . For vectors with n components:

${\displaystyle \left({\frac {1}{n}},\ldots ,{\frac {1}{n}}\right)\prec _{w}\left({\frac {1}{n-1}},\ldots ,{\frac {1}{n-1}},1\right).}$

## Geometry of majorization

For ${\displaystyle \mathbf {x} ,\mathbf {y} \in \mathbb {R} ^{n},}$ we have ${\displaystyle \mathbf {x} \prec \mathbf {y} }$ if and only if ${\displaystyle \mathbf {x} }$ is in the convex hull of all vectors obtained by permuting the coordinates of ${\displaystyle \mathbf {y} }$ .

Figure 1 displays the convex hull in 2D for the vector ${\displaystyle \mathbf {y} =(3,\,1)}$ . Notice that the center of the convex hull, which is an interval in this case, is the vector ${\displaystyle \mathbf {x} =(2,\,2)}$ . This is the "smallest" vector satisfying ${\displaystyle \mathbf {x} \prec \mathbf {y} }$ for this given vector ${\displaystyle \mathbf {y} }$ .

Figure 2 shows the convex hull in 3D. The center of the convex hull, which is a 2D polygon in this case, is the "smallest" vector ${\displaystyle \mathbf {x} }$ satisfying ${\displaystyle \mathbf {x} \prec \mathbf {y} }$ for this given vector ${\displaystyle \mathbf {y} }$ .

## Equivalent conditions

Each of the following statements is true if and only if ${\displaystyle \mathbf {a} \succ \mathbf {b} }$ :

• ${\displaystyle \mathbf {b} =D\mathbf {a} }$ for some doubly stochastic matrix ${\displaystyle D}$ (see Arnold,[1] Theorem 2.1). This is equivalent to saying ${\displaystyle \mathbf {b} }$ can be represented as a convex combination of the permutations of ${\displaystyle \mathbf {a} }$ . Furthermore the permutations require ${\displaystyle d}$ at most.[2]
• From ${\displaystyle \mathbf {a} }$ we can produce ${\displaystyle \mathbf {b} }$ by a finite sequence of "Robin Hood operations" where we replace two elements ${\displaystyle a_{i}}$ and ${\displaystyle a_{j} with ${\displaystyle a_{i}-\varepsilon }$ and ${\displaystyle a_{j}+\varepsilon }$ , respectively, for some ${\displaystyle \varepsilon \in (0,a_{i}-a_{j})}$ (see Arnold,[1] p. 11).
• For every convex function ${\displaystyle h:\mathbb {R} \to \mathbb {R} }$ , ${\displaystyle \sum _{i=1}^{d}h(a_{i})\geq \sum _{i=1}^{d}h(b_{i})}$ (see Arnold,[1] Theorem 2.9).
• ${\displaystyle \forall t\in \mathbb {R} \quad \sum _{j=1}^{d}|a_{j}-t|\geq \sum _{j=1}^{d}|b_{j}-t|}$ . (see Nielsen and Chuang Exercise 12.17,[3])

## In linear algebra

• Suppose that for two real vectors ${\displaystyle v,v'\in \mathbb {R} ^{d}}$ , ${\displaystyle v}$ majorizes ${\displaystyle v'}$ . Then it can be shown that there exists a set of probabilities ${\displaystyle (p_{1},p_{2},\ldots ,p_{d}),\sum _{i=1}^{d}p_{i}=1}$ and a set of permutations ${\displaystyle (P_{1},P_{2},\ldots ,P_{d})}$ such that ${\displaystyle v'=\sum _{i=1}^{d}p_{i}P_{i}v}$ .[2] Alternatively it can be shown that there exists a doubly stochastic matrix ${\displaystyle D}$ such that ${\displaystyle vD=v'}$
• We say that a hermitian operator, ${\displaystyle H}$ , majorizes another, ${\displaystyle H'}$ , if the set of eigenvalues of ${\displaystyle H}$ majorizes that of ${\displaystyle H'}$ .

## In recursion theory

Given ${\displaystyle f,g:\mathbb {N} \to \mathbb {N} }$ , then ${\displaystyle f}$ is said to majorize ${\displaystyle g}$ if, for all ${\displaystyle x}$ , ${\displaystyle f(x)\geq g(x)}$ . If there is some ${\displaystyle n}$ so that ${\displaystyle f(x)\geq g(x)}$ for all ${\displaystyle x>n}$ , then ${\displaystyle f}$ is said to dominate (or eventually dominate) ${\displaystyle g}$ . Alternatively, the preceding terms are often defined requiring the strict inequality ${\displaystyle f(x)>g(x)}$ instead of ${\displaystyle f(x)\geq g(x)}$ in the foregoing definitions.

## Generalizations

Various generalizations of majorization are discussed in chapters 14 and 15 of the reference work Inequalities: Theory of Majorization and Its Applications. Albert W. Marshall, Ingram Olkin, Barry Arnold. Second edition. Springer Series in Statistics. Springer, New York, 2011. ISBN 978-0-387-40087-7

## Notes

1. Barry C. Arnold. "Majorization and the Lorenz Order: A Brief Introduction". Springer-Verlag Lecture Notes in Statistics, vol. 43, 1987.
2. Xingzhi, Zhan (2003). "The sharp Rado theorem for majorizations". The American Mathematical Monthly. 110 (2): 152–153. doi:10.2307/3647776.
3. Nielsen, Michael A.; Chuang, Isaac L. (2010). Quantum Computation and Quantum Information (2nd ed.). Cambridge: Cambridge University Press. ISBN 978-1-107-00217-3. OCLC 844974180.

## References

• J. Karamata. "Sur une inegalite relative aux fonctions convexes." Publ. Math. Univ. Belgrade 1, 145158, 1932.
• G. H. Hardy, J. E. Littlewood and G. Pólya, Inequalities, 2nd edition, 1952, Cambridge University Press, London.
• Inequalities: Theory of Majorization and Its Applications Albert W. Marshall, Ingram Olkin, Barry Arnold, Second edition. Springer Series in Statistics. Springer, New York, 2011. ISBN 978-0-387-40087-7
• Inequalities: Theory of Majorization and Its Applications (1980) Albert W. Marshall, Ingram Olkin, Academic Press, ISBN 978-0-12-473750-1
• A tribute to Marshall and Olkin's book "Inequalities: Theory of Majorization and its Applications"
• Matrix Analysis (1996) Rajendra Bhatia, Springer, ISBN 978-0-387-94846-1
• Topics in Matrix Analysis (1994) Roger A. Horn and Charles R. Johnson, Cambridge University Press, ISBN 978-0-521-46713-1
• Majorization and Matrix Monotone Functions in Wireless Communications (2007) Eduard Jorswieck and Holger Boche, Now Publishers, ISBN 978-1-60198-040-3
• The Cauchy Schwarz Master Class (2004) J. Michael Steele, Cambridge University Press, ISBN 978-0-521-54677-5