Polynomial SOS

This article is about representing polynomial as sum of squares of polynomials. For representing polynomial as a sum of squares of rational functions, see Hilbert's seventeenth problem. For the sum of squares of consecutive integers, see square pyramidal number. For representing an integer as a sum of squares of integers, see Lagrange's four-square theorem.

In mathematics, a form (i.e. a homogeneous polynomial) h(x) of degree 2m in the real n-dimensional vector x is sum of squares of forms (SOS) if and only if there exist forms $g_{1}(x),\ldots ,g_{k}(x)$ of degree m such that

$h(x)=\sum _{i=1}^{k}g_{i}(x)^{2}.$

Explicit sufficient conditions for a form to be SOS have been found. However every real nonnegative form can be approximated as closely as desired (in the $l_{1}$-norm of its coefficient vector) by a sequence of forms $\{f_{\epsilon }\}$ that are SOS.

Square matricial representation (SMR)

To establish whether a form h(x) is SOS amounts to solving a convex optimization problem. Indeed, any h(x) can be written as

$h(x)=x^{\{m\}'}\left(H+L(\alpha )\right)x^{\{m\}}$

where $x^{\{m\}}$ is a vector containing a base for the forms of degree m in x (such as all monomials of degree m in x), the prime ′ denotes the transpose, H is any symmetric matrix satisfying

$h(x)=x^{\left\{m\right\}'}Hx^{\{m\}}$

and $L(\alpha )$ is a linear parameterization of the linear space

${\mathcal {L}}=\left\{L=L':~x^{\{m\}'}Lx^{\{m\}}=0\right\}.$

The dimension of the vector $x^{\{m\}}$ is given by

$\sigma (n,m)={\binom {n+m-1}{m}}$

whereas the dimension of the vector $\alpha$ is given by

$\omega (n,2m)={\frac {1}{2}}\sigma (n,m)\left(1+\sigma (n,m)\right)-\sigma (n,2m).$

Then, h(x) is SOS if and only if there exists a vector $\alpha$ such that

$H+L(\alpha )\geq 0,$

meaning that the matrix $H+L(\alpha )$ is positive-semidefinite. This is a linear matrix inequality (LMI) feasibility test, which is a convex optimization problem. The expression $h(x)=x^{\{m\}'}\left(H+L(\alpha )\right)x^{\{m\}}$ was introduced in with the name square matricial representation (SMR) in order to establish whether a form is SOS via an LMI. This representation is also known as Gram matrix.

Examples

• Consider the form of degree 4 in two variables $h(x)=x_{1}^{4}-x_{1}^{2}x_{2}^{2}+x_{2}^{4}$. We have

$m=2,~x^{\{m\}}=\left({\begin{array}{c}x_{1}^{2}\\x_{1}x_{2}\\x_{2}^{2}\end{array}}\right),~H+L(\alpha )=\left({\begin{array}{ccc}1&0&-\alpha _{1}\\0&-1+2\alpha _{1}&0\\-\alpha _{1}&0&1\end{array}}\right).$

Since there exists α such that $H+L(\alpha )\geq 0$, namely $\alpha =1$, it follows that h(x) is SOS.

• Consider the form of degree 4 in three variables $h(x)=2x_{1}^{4}-2.5x_{1}^{3}x_{2}+x_{1}^{2}x_{2}x_{3}-2x_{1}x_{3}^{3}+5x_{2}^{4}+x_{3}^{4}$. We have

$m=2,~x^{\{m\}}=\left({\begin{array}{c}x_{1}^{2}\\x_{1}x_{2}\\x_{1}x_{3}\\x_{2}^{2}\\x_{2}x_{3}\\x_{3}^{2}\end{array}}\right),~H+L(\alpha )=\left({\begin{array}{cccccc}2&-1.25&0&-\alpha _{1}&-\alpha _{2}&-\alpha _{3}\\-1.25&2\alpha _{1}&0.5+\alpha _{2}&0&-\alpha _{4}&-\alpha _{5}\\0&0.5+\alpha _{2}&2\alpha _{3}&\alpha _{4}&\alpha _{5}&-1\\-\alpha _{1}&0&\alpha _{4}&5&0&-\alpha _{6}\\-\alpha _{2}&-\alpha _{4}&\alpha _{5}&0&2\alpha _{6}&0\\-\alpha _{3}&-\alpha _{5}&-1&-\alpha _{6}&0&1\end{array}}\right).$

Since $H+L(\alpha )\geq 0$ for $\alpha =(1.18,-0.43,0.73,1.13,-0.37,0.57)$, it follows that h(x) is SOS.

Generalizations

Matrix SOS

A matrix form F(x) (i.e., a matrix whose entries are forms) of dimension r and degree 2m in the real n-dimensional vector x is SOS if and only if there exist matrix forms $G_{1}(x),\ldots ,G_{k}(x)$ of degree m such that

$F(x)=\sum _{i=1}^{k}G_{i}(x)'G_{i}(x).$

Matrix SMR

To establish whether a matrix form F(x) is SOS amounts to solving a convex optimization problem. Indeed, similarly to the scalar case any F(x) can be written according to the SMR as

$F(x)=\left(x^{\{m\}}\otimes I_{r}\right)'\left(H+L(\alpha )\right)\left(x^{\{m\}}\otimes I_{r}\right)$

where $\otimes$ is the Kronecker product of matrices, H is any symmetric matrix satisfying

$F(x)=\left(x^{\{m\}}\otimes I_{r}\right)'H\left(x^{\{m\}}\otimes I_{r}\right)$

and $L(\alpha )$ is a linear parameterization of the linear space

${\mathcal {L}}=\left\{L=L':~\left(x^{\{m\}}\otimes I_{r}\right)'L\left(x^{\{m\}}\otimes I_{r}\right)=0\right\}.$

The dimension of the vector $\alpha$ is given by

$\omega (n,2m,r)={\frac {1}{2}}r\left(\sigma (n,m)\left(r\sigma (n,m)+1\right)-(r+1)\sigma (n,2m)\right).$

Then, F(x) is SOS if and only if there exists a vector $\alpha$ such that the following LMI holds:

$H+L(\alpha )\geq 0.$

The expression $F(x)=\left(x^{\{m\}}\otimes I_{r}\right)'\left(H+L(\alpha )\right)\left(x^{\{m\}}\otimes I_{r}\right)$ was introduced in in order to establish whether a matrix form is SOS via an LMI.

Noncommutative polynomial SOS

Consider the free algebra R⟨X⟩ generated by the n noncommuting letters X = (X₁,...,X_n) and equipped with the involution ^T, such that ^T fixes R and X₁,...,X_n and reverse words formed by X₁,...,X_n. By analogy with the commutative case, the noncommutative symmetric polynomials f are the noncommutative polynomials of the form f=f^T. When any real matrix of any dimension r x r is evaluated at a symmetric noncommutative polynomial f results in a positive semi-definite matrix, f is said to be matrix-positive.

A noncommutative polynomial is SOS if there exists noncommutative polynomials $h_{1},\ldots ,h_{k}$ such that

$f(X)=\sum _{i=1}^{k}h_{i}(X)^{T}h_{i}(X).$

Surprisingly, in the noncommutative scenario a noncommutative polynomial is SoS if and only if is matrix-positive. Moreover, there exist algorithms available to decompose matrix-positive polynomials in sum of squares of noncommutative polynomials.

See also

• Sum-of-squares optimization