3,867 research outputs found
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
Exact relaxation for polynomial optimization on semi-algebraic sets
In this paper, we study the problem of computing by relaxation hierarchies
the infimum of a real polynomial function f on a closed basic semialgebraic set
and the points where this infimum is reached, if they exist. We show that when
the infimum is reached, a relaxation hierarchy constructed from the
Karush-Kuhn-Tucker ideal is always exact and that the vanishing ideal of the
KKT minimizer points is generated by the kernel of the associated moment matrix
in that degree, even if this ideal is not zero-dimensional. We also show that
this relaxation allows to detect when there is no KKT minimizer. We prove that
the exactness of the relaxation depends only on the real points which satisfy
these constraints.This exploits representations of positive polynomials as
elementsof the preordering modulo the KKT ideal, which only involves
polynomials in the initial set of variables. Applications to global
optimization, optimization on semialgebraic sets defined by regular sets of
constraints, optimization on finite semialgebraic sets, real radical
computation are given
Help on SOS
In this issue of IEEE Control Systems Magazine, Andy Packard and friends respond to a query on determining the region of attraction using sum-of-squares methods
Real root finding for equivariant semi-algebraic systems
Let be a real closed field. We consider basic semi-algebraic sets defined
by -variate equations/inequalities of symmetric polynomials and an
equivariant family of polynomials, all of them of degree bounded by .
Such a semi-algebraic set is invariant by the action of the symmetric group. We
show that such a set is either empty or it contains a point with at most
distinct coordinates. Combining this geometric result with efficient algorithms
for real root finding (based on the critical point method), one can decide the
emptiness of basic semi-algebraic sets defined by polynomials of degree
in time . This improves the state-of-the-art which is exponential
in . When the variables are quantified and the
coefficients of the input system depend on parameters , one
also demonstrates that the corresponding one-block quantifier elimination
problem can be solved in time
Minimizing Rational Functions by Exact Jacobian SDP Relaxation Applicable to Finite Singularities
This paper considers the optimization problem of minimizing a rational
function. We reformulate this problem as polynomial optimization by the
technique of homogenization. These two problems are shown to be equivalent
under some generic conditions. The exact Jacobian SDP relaxation method
proposed by Nie is used to solve the resulting polynomial optimization. We also
prove that the assumption of nonsingularity in Nie's method can be weakened as
the finiteness of singularities. Some numerical examples are given to
illustrate the efficiency of our method.Comment: 23 page
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
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