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An Approximation Bound Analysis for Lasserre’s Relaxation in Multivariate Polynomial Optimization
Suppose f, g1, ··· ,gm are multivariate polynomials in x ∈ Rn and their
degrees are at most 2d. Consider the problem:
Minimize f (x) subject to g1(x) 0, ··· ,gm(x) 0.
Let fmin (resp., fmax) be the minimum (resp., maximum) of f on the feasible set S,
and fsos be the lower bound of fmin given by Lasserre’s relaxation of order d. This
paper studies its approximation bound. Under a suitable condition on g1, ··· ,gm, we
prove that
(fmax −fsos) Q(fmax − fmin)
with Q a constant depending only on g1, ··· ,gm but not on f . In particular, if S is
the unit ball, Q = O(nd ); if S is the hypercube, Q = O(n2d ); if S is the boolean set,
Q= O(nd ).
Key words: Approximation bound ; Lasserre’s relaxation ; Polynomials ; Semidefinite programming ; Sum of squares
Jia-Wang Nie . An Approximation Bound Analysis for Lasserre’s Relaxation in Multivariate Polynomial Optimization[J]. Journal of the Operations Research Society of China, 2013 , 1(3) : 313 -332 . DOI: 10.1007/s40305-013-0017-8
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