## Polynomial Optimization 2: SOS and SDP

In this article we shall describe something called Gram-matrix method which can decompose a polynomial into sum of squares. The notation $X\succeq 0$ means $X$ is a square symmetric positive semidefinite matrix.

Proposition 1.          Let $p \in \mathbb{R}[{\bf x}]$, $\displaystyle p = \sum_{\alpha\in \mathbb{N}^n_{2d}}p_\alpha {\bf x}^\alpha$ be a polynomial of degree at most $2d$. Then the following are equivalent:

(a) $p$ is SOS

(b) $p = {\bf z}_d^T X{\bf z}_d$ for some positive semidefinite matrix $X$. The matrix $X$ is called the Gram matrix of the SOS decomposition of $p$.

(c) The following system in the matrix variable $X = (X_{\alpha,\beta})_{\alpha,\beta\in \mathbb{N}^n_{d}}$ is feasible:

(1)           $\begin{cases}X&\succeq 0\\ \displaystyle\sum_{\substack{\beta,\gamma\in \mathbb{N}^n_d\\\beta+\gamma=\alpha}}X_{\beta,\gamma}&= p_\alpha \qquad (|\alpha|\leq 2d)\end{cases}$

Before we prove it we do some counting. First, the cardinality of $\mathbb{N}^n_d$ is $\dbinom{n+d}{d}$. This is because the cardinality is indeed the number of nonnegative integer solutions $(\alpha_1,\cdots,\alpha_n,\alpha_{n+1})$ of

$\alpha_1 + \cdots + \alpha_n+\alpha_{n+1} =d$.

Using stars and bars argument, it is $\dbinom{(n+1)+d-1}{d} = \dbinom{n+d}{d}$. Thus $X$ is a $\dbinom{n+d}{d}\times\dbinom{n+d}{d}$ matrix. By similar reasoning (1) has $\dbinom{n+2d}{2d}$ equations. So (1) has polynomial size if $d$ is fixed.

Proof of Proposition 1. Set ${\bf z}_d \triangleq ({\bf x}^\alpha: |\alpha|\leq d)$. For polynomials $u_j \in \mathbb{R}[{\bf x}]_d$ define  $\text{vec}(u_j)$ as the sequence of coefficients in the monomial basis of $\mathbb{R}[{\bf x}]$. Then $u_j = \text{vec}(u_j)^T {\bf z}_d$. It follows that $\displaystyle \sum_j u_j^2 = {\bf z}_d^T \left( \sum_j \text{vec}(u_j)\text{vec}(u_j)^T\right){\bf z}_d$. The matrix $\displaystyle\sum_j \text{vec}(u_j)\text{vec}(u_j)^T$ is positive semidefinite.

Thus, $p$ is SOS if and only if $p = {\bf z}_d^T X{\bf z}_d$ for some positive semidefinite matrix $X$. The system (1) comes from equating the coefficients of polynomials $p$ and ${\bf z}_d^T X {\bf z}_d$. $\square$

Proposition 1 is useful because computing SOS decomposition of $p$ is equivalent to finding $X$ such that (1) holds. This feasibility problem is indeed a semidefinite program (SDP), which can be solved in polynomial time. Let’s recall SDP.

We first associate the Frobenius inner product to the space of matrices $\mathbb{R}^{n\times n}$ given by

$\displaystyle\langle A,B\rangle \triangleq \text{Tr}(A^T B) = \sum_{i,j=1}^n a_{ij} b_{ij}$

Denote by $\text{Sym}_n$ the vector space of symmetric $n\times n$ matrices. Given  $C,A_1,\cdots,A_m\in \text{Sym}_n$ and $b\in \mathbb{R}^m$. A (primal) semidefinite program (SDP) is in the form

(2)        $p^* = \displaystyle\sup_{X\in \text{Sym}_n} \langle C,X\rangle$ subject to $X\succeq 0$, $\langle A_j,X\rangle = b_j$ for $1\leq j \leq m$

where the decision variable is only matrix $X$.

By setting $C=0$ and choosing correct $A_j,b_j$‘s (What are they?), (2) is equivalent to finding $X$ so that (1) holds.

Let us illustrate Proposition 1 using examples.

Example 1.          Consider the degree 4 univariate polynomial $p(x) = x^4 + 4x^3 + 6x^2 + 4x + 5$.  Is $p$ SOS? Let’s try to find a decomposition. Set $X = \begin{bmatrix}a&b&c\\b&d&e\\c&e&f \end{bmatrix}$. By equating coefficients for $p = {\bf z}_2^T X {\bf z}_2$ where ${\bf z}_2 = \begin{bmatrix}1\\x\\x^2\end{bmatrix}$, system (1) is:

$X\succeq 0$

$x^4: 1 = f$

$x^3: 4 = 2e$

$x^2: 6 =d + 2 e$

$x: 4 = 2b$

$1: 5 = a$

Then by some SDP solver or by hand one solution is

$X = \begin{bmatrix} 5 & 2 & 0 \\ 2 & 6 & 2 \\ 0 & 2 & 1\end{bmatrix} = V^T V$ where $V = \begin{bmatrix} 0 & 2 & 1\\ \sqrt{2} & \sqrt{2} & 0 \\ \sqrt{3} & 0 & 0 \end{bmatrix}$.

Thus, if we set $u_1 = 2x + x^2$, $u_2 = \sqrt{2} + \sqrt{2}x$ and $u_3 = \sqrt{3}$ then

$p(x) = u_1^2+u_2^2+u_3^2 = (x^2+2x)^2 + 2(1+x)^2 + 3$.

Example 2.         Consider the degree 4 bivariate polynomial $p(x,y) = x^4 + 2 x^3 y + 3x^2 y^2 + 2xy^3 + 2y^4$. Want to find $X\succeq 0$ such that

$p = \begin{bmatrix} x^2 & xy & y^2 \end{bmatrix} X \begin{bmatrix} x^2\\xy\\y^2\end{bmatrix}$ where $X =\begin{bmatrix}a&b&c\\b&d&e\\c&e&f\end{bmatrix}$.

Equating coefficients, (1) becomes:

$X\succeq 0$

$x^4: 1 = a$

$x^3 y : 2 = 2b$

$x^2 y^2: 3= d+2c$

$xy^3: 2 = 2e$

$y^4: 2 = f$

Thus $X = \begin{bmatrix} 1 & 1 & c\\1 & 3-2c & 1\\c & 1 & 2\end{bmatrix}$. One can check $X\succeq 0$ if and only if $c\in [-1,1]$. Setting $c=-1$ and $c=0$ and factorizing $X$ we obtain two SOS decompositions of $p$:

$c = -1: p(x,y) = (x^2+xy-y^2)^2 + (y^2 + 2xy)^2$

$\displaystyle c = 0: p(x,y) = (x^2+xy)^2 + \frac{3}{2}(xy+y^2)^2 + \frac{1}{2}(xy-y^2)^2$

(End)

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