160 research outputs found
Maximum Likelihood for Matrices with Rank Constraints
Maximum likelihood estimation is a fundamental optimization problem in
statistics. We study this problem on manifolds of matrices with bounded rank.
These represent mixtures of distributions of two independent discrete random
variables. We determine the maximum likelihood degree for a range of
determinantal varieties, and we apply numerical algebraic geometry to compute
all critical points of their likelihood functions. This led to the discovery of
maximum likelihood duality between matrices of complementary ranks, a result
proved subsequently by Draisma and Rodriguez.Comment: 22 pages, 1 figur
Solving rank-constrained semidefinite programs in exact arithmetic
We consider the problem of minimizing a linear function over an affine
section of the cone of positive semidefinite matrices, with the additional
constraint that the feasible matrix has prescribed rank. When the rank
constraint is active, this is a non-convex optimization problem, otherwise it
is a semidefinite program. Both find numerous applications especially in
systems control theory and combinatorial optimization, but even in more general
contexts such as polynomial optimization or real algebra. While numerical
algorithms exist for solving this problem, such as interior-point or
Newton-like algorithms, in this paper we propose an approach based on symbolic
computation. We design an exact algorithm for solving rank-constrained
semidefinite programs, whose complexity is essentially quadratic on natural
degree bounds associated to the given optimization problem: for subfamilies of
the problem where the size of the feasible matrix is fixed, the complexity is
polynomial in the number of variables. The algorithm works under assumptions on
the input data: we prove that these assumptions are generically satisfied. We
also implement it in Maple and discuss practical experiments.Comment: Published at ISSAC 2016. Extended version submitted to the Journal of
Symbolic Computatio
Homotopy techniques for solving sparse column support determinantal polynomial systems
Let be a field of characteristic zero with
its algebraic closure. Given a sequence of polynomials
and a
polynomial matrix , with , we are interested in determining the isolated
points of , the algebraic set of points in
at which all polynomials in and all
-minors of vanish, under the assumption .
Such polynomial systems arise in a variety of applications including for
example polynomial optimization and computational geometry. We design a
randomized sparse homotopy algorithm for computing the isolated points in
which takes advantage of the determinantal
structure of the system defining . Its complexity
is polynomial in the maximum number of isolated solutions to such systems
sharing the same sparsity pattern and in some combinatorial quantities attached
to the structure of such systems. It is the first algorithm which takes
advantage both on the determinantal structure and sparsity of input
polynomials. We also derive complexity bounds for the particular but important
case where and the columns of satisfy weighted degree
constraints. Such systems arise naturally in the computation of critical points
of maps restricted to algebraic sets when both are invariant by the action of
the symmetric group
Homotopy algorithms for solving structured determinantal systems
Multivariate polynomial systems arising in numerous applications have special structures. In particular, determinantal structures and invariant systems appear in a wide range of applications such as in polynomial optimization and related questions in real algebraic geometry. The goal of this thesis is to provide efficient algorithms to solve such structured systems.
In order to solve the first kind of systems, we design efficient algorithms by using the symbolic homotopy continuation techniques. While the homotopy methods, in both numeric and symbolic, are well-understood and widely used in polynomial system solving for square systems, the use of these methods to solve over-detemined systems is not so clear. Meanwhile, determinantal systems are over-determined with more equations than unknowns. We provide probabilistic homotopy algorithms which take advantage of the determinantal structure to compute isolated points in the zero-sets of determinantal systems. The runtimes of our algorithms are polynomial in the sum of the multiplicities of isolated points and the degree of the homotopy curve. We also give the bounds on the
number of isolated points that we have to compute in three contexts: all entries of the input are in classical polynomial rings, all these polynomials are sparse, and they are weighted polynomials.
In the second half of the thesis, we deal with the problem of finding critical points of a symmetric polynomial map on an invariant algebraic set. We exploit the invariance properties of the input to split the solution space according to the orbits of the symmetric group. This allows us to design an algorithm which gives a triangular description of the solution space and which runs in time polynomial in the number of points that we have to compute. Our results are illustrated by applications in studying real algebraic sets defined by invariant polynomial systems by the means of the critical point method
On the maximal number of real embeddings of minimally rigid graphs in , and
Rigidity theory studies the properties of graphs that can have rigid
embeddings in a euclidean space or on a sphere and which in
addition satisfy certain edge length constraints. One of the major open
problems in this field is to determine lower and upper bounds on the number of
realizations with respect to a given number of vertices. This problem is
closely related to the classification of rigid graphs according to their
maximal number of real embeddings.
In this paper, we are interested in finding edge lengths that can maximize
the number of real embeddings of minimally rigid graphs in the plane, space,
and on the sphere. We use algebraic formulations to provide upper bounds. To
find values of the parameters that lead to graphs with a large number of real
realizations, possibly attaining the (algebraic) upper bounds, we use some
standard heuristics and we also develop a new method inspired by coupler
curves. We apply this new method to obtain embeddings in . One of
its main novelties is that it allows us to sample efficiently from a larger
number of parameters by selecting only a subset of them at each iteration.
Our results include a full classification of the 7-vertex graphs according to
their maximal numbers of real embeddings in the cases of the embeddings in
and , while in the case of we achieve this
classification for all 6-vertex graphs. Additionally, by increasing the number
of embeddings of selected graphs, we improve the previously known asymptotic
lower bound on the maximum number of realizations. The methods and the results
concerning the spatial embeddings are part of the proceedings of ISSAC 2018
(Bartzos et al, 2018)
Maximum likelihood geometry in the presence of data zeros
Given a statistical model, the maximum likelihood degree is the number of
complex solutions to the likelihood equations for generic data. We consider
discrete algebraic statistical models and study the solutions to the likelihood
equations when the data contain zeros and are no longer generic. Focusing on
sampling and model zeros, we show that, in these cases, the solutions to the
likelihood equations are contained in a previously studied variety, the
likelihood correspondence. The number of these solutions give a lower bound on
the ML degree, and the problem of finding critical points to the likelihood
function can be partitioned into smaller and computationally easier problems
involving sampling and model zeros. We use this technique to compute a lower
bound on the ML degree for tensors of border
rank and tables of rank for ,
the first four values of for which the ML degree was previously unknown
Numerical Schubert calculus
We develop numerical homotopy algorithms for solving systems of polynomial
equations arising from the classical Schubert calculus. These homotopies are
optimal in that generically no paths diverge. For problems defined by
hypersurface Schubert conditions we give two algorithms based on extrinsic
deformations of the Grassmannian: one is derived from a Gr\"obner basis for the
Pl\"ucker ideal of the Grassmannian and the other from a SAGBI basis for its
projective coordinate ring. The more general case of special Schubert
conditions is solved by delicate intrinsic deformations, called Pieri
homotopies, which first arose in the study of enumerative geometry over the
real numbers. Computational results are presented and applications to control
theory are discussed.Comment: 24 pages, LaTeX 2e with 2 figures, used epsf.st
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