6,387 research outputs found
An error analysis for polynomial optimization over the simplex based on the multivariate hypergeometric distribution
We study the minimization of fixed-degree polynomials over the simplex. This
problem is well-known to be NP-hard, as it contains the maximum stable set
problem in graph theory as a special case. In this paper, we consider a
rational approximation by taking the minimum over the regular grid, which
consists of rational points with denominator (for given ). We show that
the associated convergence rate is for quadratic polynomials. For
general polynomials, if there exists a rational global minimizer over the
simplex, we show that the convergence rate is also of the order . Our
results answer a question posed by De Klerk et al. (2013) and improves on
previously known bounds in the quadratic case.Comment: 17 page
Lower Bounds for Real Solutions to Sparse Polynomial Systems
We show how to construct sparse polynomial systems that have non-trivial
lower bounds on their numbers of real solutions. These are unmixed systems
associated to certain polytopes. For the order polytope of a poset P this lower
bound is the sign-imbalance of P and it holds if all maximal chains of P have
length of the same parity. This theory also gives lower bounds in the real
Schubert calculus through sagbi degeneration of the Grassmannian to a toric
variety, and thus recovers a result of Eremenko and Gabrielov.Comment: 31 pages. Minor revision
Convergence analysis of a Lasserre hierarchy of upper bounds for polynomial minimization on the sphere
We study the convergence rate of a hierarchy of upper bounds for polynomial
minimization problems, proposed by Lasserre [SIAM J. Optim. 21(3) (2011), pp.
864-885], for the special case when the feasible set is the unit (hyper)sphere.
The upper bound at level r of the hierarchy is defined as the minimal expected
value of the polynomial over all probability distributions on the sphere, when
the probability density function is a sum-of-squares polynomial of degree at
most 2r with respect to the surface measure.
We show that the exact rate of convergence is Theta(1/r^2), and explore the
implications for the related rate of convergence for the generalized problem of
moments on the sphere.Comment: 14 pages, 2 figure
Compute-and-Forward: Finding the Best Equation
Compute-and-Forward is an emerging technique to deal with interference. It
allows the receiver to decode a suitably chosen integer linear combination of
the transmitted messages. The integer coefficients should be adapted to the
channel fading state. Optimizing these coefficients is a Shortest Lattice
Vector (SLV) problem. In general, the SLV problem is known to be prohibitively
complex. In this paper, we show that the particular SLV instance resulting from
the Compute-and-Forward problem can be solved in low polynomial complexity and
give an explicit deterministic algorithm that is guaranteed to find the optimal
solution.Comment: Paper presented at 52nd Allerton Conference, October 201
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