Results 1  10
of
162
Global Optimization with Polynomials and the Problem of Moments
 SIAM Journal on Optimization
, 2001
"... We consider the problem of finding the unconstrained global minimum of a realvalued polynomial p(x) : R R, as well as the global minimum of p(x), in a compact set K defined by polynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear mat ..."
Abstract

Cited by 319 (33 self)
 Add to MetaCart
We consider the problem of finding the unconstrained global minimum of a realvalued polynomial p(x) : R R, as well as the global minimum of p(x), in a compact set K defined by polynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear matrix inequality (LMI) problems. A notion of KarushKuhnTucker polynomials is introduced in a global optimality condition. Some illustrative examples are provided. Key words. global optimization, theory of moments and positive polynomials, semidefinite programming AMS subject classifications. 90C22, 90C25 PII. S1052623400366802 1.
A comparison of the SheraliAdams, LovászSchrijver and Lasserre relaxations for 01 programming
 Mathematics of Operations Research
, 2001
"... ..."
Sums of Squares and Semidefinite Programming Relaxations for Polynomial Optimization Problems with Structured Sparsity
 SIAM Journal on Optimization
, 2006
"... Abstract. Unconstrained and inequality constrained sparse polynomial optimization problems (POPs) are considered. A correlative sparsity pattern graph is defined to find a certain sparse structure in the objective and constraint polynomials of a POP. Based on this graph, sets of supports for sums of ..."
Abstract

Cited by 76 (24 self)
 Add to MetaCart
Abstract. Unconstrained and inequality constrained sparse polynomial optimization problems (POPs) are considered. A correlative sparsity pattern graph is defined to find a certain sparse structure in the objective and constraint polynomials of a POP. Based on this graph, sets of supports for sums of squares (SOS) polynomials that lead to efficient SOS and semidefinite programming (SDP) relaxations are obtained. Numerical results from various test problems are included to show the improved performance of the SOS and SDP relaxations. Key words.
The truncated complex Kmoment problem
 Trans. Amer. Math. Soc
"... on the occasion of his eightyseventh birthday Abstract. Let γ ≡ γ (2n) denote a sequence of complex numbers γ00,γ01,γ10,...,γ0,2n,...,γ2n,0 (γ00> 0,γij = ¯γji), and let K denote a closed subset of the complex plane C. The Truncated Complex KMoment Problem for γ entails determining whether there ..."
Abstract

Cited by 44 (5 self)
 Add to MetaCart
on the occasion of his eightyseventh birthday Abstract. Let γ ≡ γ (2n) denote a sequence of complex numbers γ00,γ01,γ10,...,γ0,2n,...,γ2n,0 (γ00> 0,γij = ¯γji), and let K denote a closed subset of the complex plane C. The Truncated Complex KMoment Problem for γ entails determining whether there exists a positive Borel measure µ on C such that γij = ∫ ¯z izj dµ (0 ≤ i + j ≤ 2n) and supp µ ⊆ K. For K ≡ KP a semialgebraic set determined by a collection of complex polynomials P = {pi (z, ¯z)} m i=1, we characterize the existence of a finitely atomic representing measure with the fewest possible atoms in terms of positivity and extension properties of the moment matrix M (n)(γ) and the localizing matrices Mp i. We prove that there exists a rank M (n)atomic representing measure for γ (2n) supported in KP if and only if M (n) ≥ 0andthereissomerankpreserving extension M (n +1)forwhichMp i (n + ki) ≥ 0, where deg pi =2ki or 2ki − 1(1 ≤ i ≤ m). 1.
Optimization of polynomials on compact semialgebraic sets
 SIAM J. Optim
"... Abstract. A basic closed semialgebraic subset S of R n is defined by simultaneous polynomial inequalities g1 ≥ 0,..., gm ≥ 0. We give a short introduction to Lasserre’s method for minimizing a polynomial f on a compact set S of this kind. It consists of successively solving tighter and tighter conve ..."
Abstract

Cited by 41 (4 self)
 Add to MetaCart
Abstract. A basic closed semialgebraic subset S of R n is defined by simultaneous polynomial inequalities g1 ≥ 0,..., gm ≥ 0. We give a short introduction to Lasserre’s method for minimizing a polynomial f on a compact set S of this kind. It consists of successively solving tighter and tighter convex relaxations of this problem which can be formulated as semidefinite programs. We give a new short proof for the convergence of the optimal values of these relaxations to the infimum f ∗ of f on S which is constructive and elementary. In the case where f possesses a unique minimizer x ∗ , we prove that every sequence of “nearly ” optimal solutions of the successive relaxations gives rise to a sequence of points in R n converging to x ∗. 1. Introduction to Lasserre’s method Throughout the paper, we suppose 1 ≤ n ∈ N and abbreviate (X1,..., Xn) by ¯X. We let R [ ¯ X] denote the ring of real polynomials in n indeterminates. Suppose we are given a so called basic closed semialgebraic set, i.e., a set S: = {x ∈ R n  g1(x) ≥ 0,..., gm(x) ≥ 0}
Semidefinite Representations for Finite Varieties
 MATHEMATICAL PROGRAMMING
, 2002
"... We consider the problem of minimizing a polynomial over a semialgebraic set defined by polynomial equalities and inequalities. When the polynomial equalities have a finite number of complex solutions and define a radical ideal we can reformulate this problem as a semidefinite programming prob ..."
Abstract

Cited by 36 (6 self)
 Add to MetaCart
We consider the problem of minimizing a polynomial over a semialgebraic set defined by polynomial equalities and inequalities. When the polynomial equalities have a finite number of complex solutions and define a radical ideal we can reformulate this problem as a semidefinite programming problem. This semidefinite program involves combinatorial moment matrices, which are matrices indexed by a basis of the quotient vector space R[x 1 , . . . , x n ]/I. Our arguments are elementary and extend known facts for the grid case including 0/1 and polynomial programming. They also relate to known algebraic tools for solving polynomial systems of equations with finitely many complex solutions. Semidefinite approximations can be constructed by considering truncated combinatorial moment matrices; rank conditions are given (in a grid case) that ensure that the approximation solves the original problem at optimality.
Feedback Control of Quantum State Reduction
, 2004
"... Feedback control of quantum mechanical systems must take into account the probabilistic nature of quantum measurement. We formulate quantum feedback control as a problem of stochastic nonlinear control by considering separately a quantum filtering problem and a state feedback control problem for th ..."
Abstract

Cited by 35 (3 self)
 Add to MetaCart
Feedback control of quantum mechanical systems must take into account the probabilistic nature of quantum measurement. We formulate quantum feedback control as a problem of stochastic nonlinear control by considering separately a quantum filtering problem and a state feedback control problem for the filter. We explore the use of stochastic Lyapunov techniques for the design of feedback controllers for quantum spin systems and demonstrate the possibility of stabilizing one outcome of a quantum measurement with unit probability.
Convergent SDPRelaxations in Polynomial Optimization with Sparsity
 SIAM Journal on Optimization
"... Abstract. We consider a polynomial programming problem P on a compact semialgebraic set K ⊂ R n, described by m polynomial inequalities gj(X) ≥ 0, and with criterion f ∈ R[X]. We propose a hierarchy of semidefinite relaxations in the spirit those of Waki et al. [9]. In particular, the SDPrelaxati ..."
Abstract

Cited by 29 (8 self)
 Add to MetaCart
Abstract. We consider a polynomial programming problem P on a compact semialgebraic set K ⊂ R n, described by m polynomial inequalities gj(X) ≥ 0, and with criterion f ∈ R[X]. We propose a hierarchy of semidefinite relaxations in the spirit those of Waki et al. [9]. In particular, the SDPrelaxation of order r has the following two features: (a) The number of variables is O(κ 2r) where κ = max[κ1, κ2] witth κ1 (resp. κ2) being the maximum number of variables appearing the monomials of f (resp. appearing in a single constraint gj(X) ≥ 0). (b) The largest size of the LMI’s (Linear Matrix Inequalities) is O(κ r). This is to compare with the respective number of variables O(n 2r) and LMI size O(n r) in the original SDPrelaxations defined in [11]. Therefore, great computational savings are expected in case of sparsity in the data {gj, f}, i.e. when κ is small, a frequent case in practical applications of interest. The novelty with respect to [9] is that we prove convergence to the global optimum of P when the sparsity pattern satisfies a condition often encountered in large size problems of practical applications, and known as the running intersection property in graph theory. In such cases, and as a byproduct, we also obtain a new representation result for polynomials positive on a basic closed semialgebraic set, a sparse version of Putinar’s Positivstellensatz [16]. 1.
New upper bounds for kissing numbers from semidefinite programming
 Journal of the American Mathematical Society
, 2006
"... In geometry, the kissing number problem asks for the maximum number τn of unit spheres that can simultaneously touch the unit sphere in ndimensional Euclidean space without pairwise overlapping. The value of τn is only known for n =1, 2, 3, 4, 8, 24. While its determination for n =1, 2 is trivial, ..."
Abstract

Cited by 28 (11 self)
 Add to MetaCart
In geometry, the kissing number problem asks for the maximum number τn of unit spheres that can simultaneously touch the unit sphere in ndimensional Euclidean space without pairwise overlapping. The value of τn is only known for n =1, 2, 3, 4, 8, 24. While its determination for n =1, 2 is trivial, it is not the case
A Representation Theorem for Certain Partially Ordered Commutative Rings
"... Let A be a commutative ring with 1, let P A be a preordering of higher level (i.e. 0; 1 2 P , 1 62 P , P + P P , P P P and A 2n P for some n 2 N) and let M A be an archimedean Pmodule (i.e. 1 2 M , 1 62 M , M +M M , P M M and 8 a 2 A 9n 2 N n a 2 M ). We endow X(M) := f' 2 Hom(A ..."
Abstract

Cited by 28 (1 self)
 Add to MetaCart
Let A be a commutative ring with 1, let P A be a preordering of higher level (i.e. 0; 1 2 P , 1 62 P , P + P P , P P P and A 2n P for some n 2 N) and let M A be an archimedean Pmodule (i.e. 1 2 M , 1 62 M , M +M M , P M M and 8 a 2 A 9n 2 N n a 2 M ). We endow X(M) := f' 2 Hom(A;R) j '(M) R+ g with the weak topology with respect to all mappings ba : X(M) ! R, ba(') := '(a) and consider the representation M : A ! C(X(M);R), a 7! ba. We nd that X(M) is a nonempty compact Hausdorff space. Further we prove that 1 M (C + (X(M);R)) = fa 2 A j 8n 2 N 9 k 2 N k(1 + na) 2 Mg and ker( M ) = fa 2 A j 8n 2 N 9 k 2 N k(1 na) 2 Mg. (By C + (X(M);R) we denote the set of all continuous functions which do not take negative values.)