39,103 research outputs found
Relative Entropy Relaxations for Signomial Optimization
Signomial programs (SPs) are optimization problems specified in terms of
signomials, which are weighted sums of exponentials composed with linear
functionals of a decision variable. SPs are non-convex optimization problems in
general, and families of NP-hard problems can be reduced to SPs. In this paper
we describe a hierarchy of convex relaxations to obtain successively tighter
lower bounds of the optimal value of SPs. This sequence of lower bounds is
computed by solving increasingly larger-sized relative entropy optimization
problems, which are convex programs specified in terms of linear and relative
entropy functions. Our approach relies crucially on the observation that the
relative entropy function -- by virtue of its joint convexity with respect to
both arguments -- provides a convex parametrization of certain sets of globally
nonnegative signomials with efficiently computable nonnegativity certificates
via the arithmetic-geometric-mean inequality. By appealing to representation
theorems from real algebraic geometry, we show that our sequences of lower
bounds converge to the global optima for broad classes of SPs. Finally, we also
demonstrate the effectiveness of our methods via numerical experiments
Global optimization of polynomials using gradient tentacles and sums of squares
In this work, the combine the theory of generalized critical values with the
theory of iterated rings of bounded elements (real holomorphy rings).
We consider the problem of computing the global infimum of a real polynomial
in several variables. Every global minimizer lies on the gradient variety. If
the polynomial attains a minimum, it is therefore equivalent to look for the
greatest lower bound on its gradient variety. Nie, Demmel and Sturmfels proved
recently a theorem about the existence of sums of squares certificates for such
lower bounds. Based on these certificates, they find arbitrarily tight
relaxations of the original problem that can be formulated as semidefinite
programs and thus be solved efficiently.
We deal here with the more general case when the polynomial is bounded from
belo w but does not necessarily attain a minimum. In this case, the method of
Nie, Demmel and Sturmfels might yield completely wrong results. In order to
overcome this problem, we replace the gradient variety by larger semialgebraic
sets which we call gradient tentacles. It now gets substantially harder to
prove the existence of the necessary sums of squares certificates.Comment: 22 page
Exploiting symmetries in SDP-relaxations for polynomial optimization
In this paper we study various approaches for exploiting symmetries in
polynomial optimization problems within the framework of semi definite
programming relaxations. Our special focus is on constrained problems
especially when the symmetric group is acting on the variables. In particular,
we investigate the concept of block decomposition within the framework of
constrained polynomial optimization problems, show how the degree principle for
the symmetric group can be computationally exploited and also propose some
methods to efficiently compute in the geometric quotient.Comment: (v3) Minor revision. To appear in Math. of Operations Researc
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