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Sum of squares methods for minimizing polynomial forms over spheres and hypersurfaces |
Jiawang NIE() |
Department of Mathematics, University of California, San Diego, La Jolla, CA 92093, USA |
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Abstract This paper studies the problem of minimizing a homogeneous polynomial (form) f(x) over the unit sphere Sn-1={x∈?n:‖x‖2=|1}. The problem is NP-hard when f(x) has degree 3 or higher. Denote by fmin (resp. fmax) the minimum (resp. maximum) value of f(x) on Sn-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 fmin:max? γ s.t. f(x)-γ·‖x‖22d? is SOS.Let fsos be the above optimal value. Then we show that for all n≥2d,1≤fmax?-fsosfmax?-fmin?≤C(d)(n2d).Here, the constant C(d) is independent of n. Second, when f(x) is a multi-form and Sn-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∈?n: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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Keywords
Approximation bound
form
hypersurface
L2-norm
G-norm
multi-form
polynomial
semidefinite programming
sum of squares
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Corresponding Author(s):
NIE Jiawang,Email:njw@math.ucsd.edu
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Issue Date: 01 April 2012
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