Results 11  20
of
51
Certifying Convergence of Lasserre’s Hierarchy via Flat Truncation
, 2013
"... Abstract. Consider the optimization problem of minimizing a polynomial function subject to polynomial constraints. A typical approach for solving it globally is applying Lasserre’s hierarchy of semidefinite relaxations, based on either Putinar’s or Schmüdgen’s Positivstellensatz. A practical questi ..."
Abstract

Cited by 14 (7 self)
 Add to MetaCart
Abstract. Consider the optimization problem of minimizing a polynomial function subject to polynomial constraints. A typical approach for solving it globally is applying Lasserre’s hierarchy of semidefinite relaxations, based on either Putinar’s or Schmüdgen’s Positivstellensatz. A practical question in applications is: how to certify its convergence and get minimizers? In this paper, we propose flat truncation as a certificate for this purpose. Assume the set of global minimizers is nonempty and finite. Our main results are: i) Putinar type Lasserre’s hierarchy has finite convergence if and only if flat truncation holds, under some generic assumptions; the same conclusion holds for the Schmüdgen type one under weaker assumptions. ii) Flat truncation is asymptotically satisfied for Putinar type Lasserre’s hierarchy if the archimedean condition holds; the same conclusion holds for the Schmüdgen type one if the feasible set is compact. iii) We show that flat truncation can be used as a certificate to check exactness of standard SOS relaxations and Jacobian SDP relaxations.
Expressing Combinatorial Optimization Problems by Systems of Polynomial Equations and the Nullstellensatz
, 2007
"... Systems of polynomial equations over the complex or real numbers can be used to model combinatorial problems. In this way, a combinatorial problem is feasible (e.g. a graph is 3colorable, hamiltonian, etc.) if and only if a related system of polynomial equations has a solution. In the first part of ..."
Abstract

Cited by 14 (8 self)
 Add to MetaCart
Systems of polynomial equations over the complex or real numbers can be used to model combinatorial problems. In this way, a combinatorial problem is feasible (e.g. a graph is 3colorable, hamiltonian, etc.) if and only if a related system of polynomial equations has a solution. In the first part of this paper, we construct new polynomial encodings for the problems of finding in a graph its longest cycle, the largest planar subgraph, the edgechromatic number, or the largest kcolorable subgraph. For an infeasible polynomial system, the (complex) Hilbert Nullstellensatz gives a certificate that the associated combinatorial problem is infeasible. Thus, unless P = NP, there must exist an infinite sequence of infeasible instances of each hard combinatorial problem for which the minimum degree of a Hilbert Nullstellensatz certificate of the associated polynomial system grows. We show that the minimumdegree of a Nullstellensatz certificate for the nonexistence of a stable set of size greater than the stability number of the graph is the stability number of the graph. Moreover, such a certificate contains at least one term per stable set of G. In contrast, for non3colorability, we found only graphs with Nullstellensatz certificates of degree four.
Global minimization of rational functions and the nearest GCDs
 J. of Global Optimization
"... This paper discusses the global minimization of rational functions with or without constraints. The sum of squares (SOS) relaxations are proposed to find the global minimum and minimizers. Some special features of the SOS relaxations are studied. As an application, we show how to find the nearest co ..."
Abstract

Cited by 12 (0 self)
 Add to MetaCart
(Show Context)
This paper discusses the global minimization of rational functions with or without constraints. The sum of squares (SOS) relaxations are proposed to find the global minimum and minimizers. Some special features of the SOS relaxations are studied. As an application, we show how to find the nearest common divisors of polynomials via global minimization of rational functions.
Semidefinite characterization and computation of real radical ideals
 Foundations of Computational Mathematics
"... For an ideal I ⊆ R[x] given by a set of generators, a new semidefinite characterization of its real radical I(VR(I)) is presented, provided it is zerodimensional (even if I is not). Moreover we propose an algorithm using numerical linear algebra and semidefinite optimization techniques, to compute ..."
Abstract

Cited by 12 (8 self)
 Add to MetaCart
(Show Context)
For an ideal I ⊆ R[x] given by a set of generators, a new semidefinite characterization of its real radical I(VR(I)) is presented, provided it is zerodimensional (even if I is not). Moreover we propose an algorithm using numerical linear algebra and semidefinite optimization techniques, to compute all (finitely many) points of the real variety VR(I) as well as a set of generators of the real radical ideal. The latter is obtained in the form of a border or Gröbner basis. The algorithm is based on moment relaxations and, in contrast to other existing methods, it exploits the real algebraic nature of the problem right from the beginning and avoids the computation of complex components. AMS: 14P05 13P10 12E12 12D10 90C22 1
LMI approximations for cones of positive semidefinite forms
 Fachbereich Mathematik, Universität Konstanz, 78457
"... An interesting recent trend in optimization is the application of semidefinite programming techniques to new classes of optimization problems. In particular, this trend has been successful in showing that under suitable circumstances, polynomial optimization problems can be approximated via a sequen ..."
Abstract

Cited by 11 (4 self)
 Add to MetaCart
(Show Context)
An interesting recent trend in optimization is the application of semidefinite programming techniques to new classes of optimization problems. In particular, this trend has been successful in showing that under suitable circumstances, polynomial optimization problems can be approximated via a sequence of semidefinite programs. Similar ideas apply to conic optimization over the cone of copositive matrices, and to certain optimization problems involving random variables with some known moment information. We bring together several of these approximation results by studying the approximability of cones of positive semidefinite forms (homogeneous polynomials). Our approach enables us to extend the existing methodology to new approximation schemes. In particular, we derive a novel approximation to the cone of copositive forms, that is, the cone of forms that are positive semidefinite over the nonnegative orthant. The format of our construction can be extended to forms that are positive semidefinite over more general conic domains. We also construct polyhedral approximations to cones of positive semidefinite forms over a polyhedral domain. This opens the possibility of using linear programming technology in optimization problems over these cones.
Parametric Optimization and Optimal Control using Algebraic Geometry
 International Journal of Control
, 2006
"... We present two algebraic methods to solve the parametric optimization problem that arises in nonlinear model predictive control. We consider constrained discretetime polynomial systems and the corresponding constrained finitetime optimal control problem. The first method is based on cylindrical al ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
(Show Context)
We present two algebraic methods to solve the parametric optimization problem that arises in nonlinear model predictive control. We consider constrained discretetime polynomial systems and the corresponding constrained finitetime optimal control problem. The first method is based on cylindrical algebraic decomposition. The second uses Gröbner bases and the eigenvalue method for solving systems of polynomial equations. Both methods aim at moving most of the computational burden associated with the optimization problem offline, by precomputing certain algebraic objects. Then, an online algorithm uses this precomputed information to obtain the solution of the original optimization problem in real time fast and efficiently. Introductory material is provided as appropriate and the algorithms are accompanied by illustrative examples. 1
Computer Algebra, Combinatorics, and Complexity: Hilbert’s Nullstellensatz and NPcomplete Problems
, 2008
"... ..."
Convex hulls of algebraic sets
, 2010
"... This article describes a method to compute successive convex approximations of the convex hull of a set of points in Rn that are the solutions to a system of polynomial equations over the reals. The method relies on sums of squares of polynomials and the dual theory of moment matrices. The main fea ..."
Abstract

Cited by 10 (1 self)
 Add to MetaCart
This article describes a method to compute successive convex approximations of the convex hull of a set of points in Rn that are the solutions to a system of polynomial equations over the reals. The method relies on sums of squares of polynomials and the dual theory of moment matrices. The main feature of the technique is that all computations are done modulo the ideal generated by the polynomials defining the set to the convexified. This work was motivated by questions raised by Lovász concerning extensions of the theta body of a graph to arbitrary real algebraic varieties, and hence the relaxations described here are called theta bodies. The convexification process can be seen as an incarnation of Lasserre’s hierarchy of convex relaxations of a semialgebraic set in Rn. When the defining ideal is real radical the results become especially nice. We provide several examples of the method and discuss convergence issues. Finite convergence, especially after the first step of the method, can be described explicitly for finite point sets.
EXPLOITING SYMMETRIES IN SDPRELAXATIONS FOR POLYNOMIAL OPTIMIZATION
"... In this paper we study various approaches for exploiting symmetries in polynomial optimization problems within the framework of semidefinite programming relaxations. Our special focus is on constrained problems especially when the symmetric group is acting on the variables. In particular, we investi ..."
Abstract

Cited by 9 (2 self)
 Add to MetaCart
(Show Context)
In this paper we study various approaches for exploiting symmetries in polynomial optimization problems within the framework of semidefinite 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.
Error bounds for some semidefinite programming approaches to polynomial minimization on the hypercube
"... We consider the problem of minimizing a polynomial on the hypercube [0, 1] n and derive new error bounds for the hierarchy of semidefinite programming approximations to this problem corresponding to the Positivstellensatz of Schmüdgen [26]. The main tool we employ is Bernstein approximations of po ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
We consider the problem of minimizing a polynomial on the hypercube [0, 1] n and derive new error bounds for the hierarchy of semidefinite programming approximations to this problem corresponding to the Positivstellensatz of Schmüdgen [26]. The main tool we employ is Bernstein approximations of polynomials, which also gives constructive proofs and degree bounds for positivity certificates on the hypercube.