An exact Jacobian SDP relaxation for polynomial optimization

Abstract

Given polynomials f (x), g _i (x), h _j (x), we study how to minimize f (x) on the set

$$S = \left\{ x \in \mathbb{R}^n:\, h_1(x) = \cdots = h_{m_1}(x) = 0,\\ g_1(x)\geq 0, \ldots, g_{m_2}(x) \geq 0 \right\}.$$

Let f _min be the minimum of f on S. Suppose S is nonsingular and f _min is achievable on S, which are true generically. This paper proposes a new type semidefinite programming (SDP) relaxation which is the first one for solving this problem exactly. First, we construct new polynomials ${\varphi_1, \ldots, \varphi_r}$ , by using the Jacobian of f, h _i , g _j , such that the above problem is equivalent to

$$\begin{gathered}\underset{x\in\mathbb{R}^n}{\min} f(x) \hfill \\ \, \, {\rm s.t.}\; h_i(x) = 0, \, \varphi_j(x) = 0, \, 1\leq i \leq m_1, 1 \leq j \leq r, \hfill \\ \quad \, \, \, g_1(x)^{\nu_1}\cdots g_{m_2}(x)^{\nu_{m_2}}\geq 0, \, \quad\forall\, \nu \,\in \{0,1\}^{m_2} .\hfill \end{gathered}$$

Second, we prove that for all N big enough, the standard N-th order Lasserre’s SDP relaxation is exact for solving this equivalent problem, that is, its optimal value is equal to f _min. Some variations and examples are also shown.