Polynomial SOS

• Consider the form of degree 4 in two variables ${\displaystyle h(x)=x_{1}^{4}-x_{1}^{2}x_{2}^{2}+x_{2}^{4}}$. We have
  $${\displaystyle m=2,~x^{\{m\}}={\begin{pmatrix}x_{1}^{2}\\x_{1}x_{2}\\x_{2}^{2}\end{pmatrix}}\!,~H+L(\alpha )={\begin{pmatrix}1&0&-\alpha _{1}\\0&-1+2\alpha _{1}&0\\-\alpha _{1}&0&1\end{pmatrix}}\!.}$$

  
  Since there exists α such that ${\displaystyle H+L(\alpha )\geq 0}$, namely ${\displaystyle \alpha =1}$, it follows that h(x) is SOS.
• Consider the form of degree 4 in three variables ${\displaystyle h(x)=2x_{1}^{4}-2.5x_{1}^{3}x_{2}+x_{1}^{2}x_{2}x_{3}-2x_{1}x_{3}^{3}+5x_{2}^{4}+x_{3}^{4}}$. We have
  $${\displaystyle m=2,~x^{\{m\}}={\begin{pmatrix}x_{1}^{2}\\x_{1}x_{2}\\x_{1}x_{3}\\x_{2}^{2}\\x_{2}x_{3}\\x_{3}^{2}\end{pmatrix}},~H+L(\alpha )={\begin{pmatrix}2&-1.25&0&-\alpha _{1}&-\alpha _{2}&-\alpha _{3}\\-1.25&2\alpha _{1}&0.5+\alpha _{2}&0&-\alpha _{4}&-\alpha _{5}\\0&0.5+\alpha _{2}&2\alpha _{3}&\alpha _{4}&\alpha _{5}&-1\\-\alpha _{1}&0&\alpha _{4}&5&0&-\alpha _{6}\\-\alpha _{2}&-\alpha _{4}&\alpha _{5}&0&2\alpha _{6}&0\\-\alpha _{3}&-\alpha _{5}&-1&-\alpha _{6}&0&1\end{pmatrix}}.}$$

  
  Since ${\displaystyle H+L(\alpha )\geq 0}$ for ${\displaystyle \alpha =(1.18,-0.43,0.73,1.13,-0.37,0.57)}$, it follows that h(x) is 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 ${\displaystyle G_{1}(x),\ldots ,G_{k}(x)}$ of degree m such that

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

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

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

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

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

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

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

The dimension of the vector ${\displaystyle \alpha }$ is given by

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

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

$${\displaystyle H+L(\alpha )\geq 0.}$$

The expression ${\displaystyle F(x)=\left(x^{\{m\}}\otimes I_{r}\right)'\left(H+L(\alpha )\right)\left(x^{\{m\}}\otimes I_{r}\right)}$ was introduced in [9] in order to establish whether a matrix form is SOS via an LMI.

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 reverses 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 × 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 ${\displaystyle h_{1},\ldots ,h_{k}}$ such that

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

Surprisingly, in the noncommutative scenario a noncommutative polynomial is SOS if and only if it is matrix-positive.[10] Moreover, there exist algorithms available to decompose matrix-positive polynomials in sum of squares of noncommutative polynomials.[11]