Abstract
This paper studies the problem of minimizing a homogeneous polynomial (form) f(x) over the unit sphere \(\mathbb{S}^{n - 1} = \left\{ {x \in \mathbb{R}^n :\left\| x \right\|_2 = 1} \right\}\). The problem is NP-hard when f(x) has degree 3 or higher. Denote by f min (resp. f max) the minimum (resp. maximum) value of f(x) on \(\mathbb{S}^{n - 1}\). First, when f(x) is an even form of degree 2d, we study the standard sum of squares (SOS) relaxation for finding a lower bound of the minimum f min:
Let f sos be the above optimal value. Then we show that for all n ⩾ 2d,
Here, the constant C(d) is independent of n. Second, when f(x) is a multi-form and \(\mathbb{S}^{n - 1}\) becomes a multi-unit sphere, we generalize the above SOS relaxation and prove a similar bound. Third, when f(x) is sparse, we prove an improved bound depending on its sparsity pattern; when f(x) is odd, we formulate the problem equivalently as minimizing a certain even form, and prove a similar bound. Last, for minimizing f(x) over a hypersurface H(g) = {x ∈ ℝn: g(x) = 1} defined by a positive definite form g(x), we generalize the above SOS relaxation and prove a similar bound.
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Nie, J. Sum of squares methods for minimizing polynomial forms over spheres and hypersurfaces. Front. Math. China 7, 321–346 (2012). https://doi.org/10.1007/s11464-012-0187-4
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DOI: https://doi.org/10.1007/s11464-012-0187-4
Keywords
- Approximation bound
- form
- hypersurface
- L 2-norm
- G-norm
- multi-form
- polynomial
- semidefinite programming
- sum of squares