Semidefinite Representation Research Articles

An SDP method for fractional semi-infinite programming problems with SOS-convex polynomials

In this paper, we study a class of fractional semi-infinite polynomial programming problems involving sos-convex polynomial functions. For such a problem, by a conic reformulation proposed in our previous work and the quadratic modules associated with the index set, a hierarchy of semidefinite programming (SDP) relaxations can be constructed and convergent upper bounds of the optimum can be obtained. In this paper, by introducing Lasserre’s measure-based representation of nonnegative polynomials on the index set to the conic reformulation, we present a new SDP relaxation method for the considered problem. This method enables us to compute convergent lower bounds of the optimum and extract approximate minimizers. Moreover, for a set defined by infinitely many sos-convex polynomial inequalities, we obtain a procedure to construct a convergent sequence of outer approximations which have semidefinite representations (SDr). The convergence rate of the lower bounds and outer SDr approximations are also discussed.

Semi-definite Representations for Sets of Cubics on the Two-dimensional Sphere

Semi-definite Representations for Sets of Cubics on the Two-dimensional Sphere

Hyperbolic secant varieties of M-curves

Abstract We relate the geometry of curves to the notion of hyperbolicity in real algebraic geometry. A hyperbolic variety is a real algebraic variety that (in particular) admits a real fibered morphism to a projective space whose dimension is equal to the dimension of the variety. We study hyperbolic varieties with a special interest in the case of hypersurfaces that admit a real algebraic ruling. The central part of the paper is concerned with secant varieties of real algebraic curves where the real locus has the maximal number of connected components, which is determined by the genus of the curve. For elliptic normal curves, we further obtain definite symmetric determinantal representations for the hyperbolic secant hypersurfaces, which implies the existence of symmetric Ulrich sheaves of rank one on these hypersurfaces. We also use this to derive better bounds on the size of semidefinite representations for convex hulls of real algebraic curves of genus 1.

The Set of Separable States has no Finite Semidefinite Representation Except in Dimension 3times 2

Given integers n ge m, let text {Sep}(n,m) be the set of separable states on the Hilbert space mathbb {C}^n otimes mathbb {C}^m. It is well-known that for (n,m)=(3,2) the set of separable states has a simple description using semidefinite programming: it is given by the set of states that have a positive partial transpose. In this paper we show that for larger values of n and m the set text {Sep}(n,m) has no semidefinite programming description of finite size. As text {Sep}(n,m) is a semialgebraic set this provides a new counterexample to the Helton–Nie conjecture, which was recently disproved by Scheiderer in a breakthrough result. Compared to Scheiderer’s approach, our proof is elementary and relies only on basic results about semialgebraic sets and functions.

New Formulation and Computation of NRDF for Time-Varying Multivariate Gaussian Processes With Correlated Noise

We derive a new formulation of nonanticipative rate distortion function (NRDF) for time-varying multivariate Gauss-Markov processes driven by correlated noise described by a first order autoregressive moving average (ARMA(1,1)) process with mean-squared error (MSE) distortion constraint. To arrive to this formulation, we first show that the Gauss-Markov process with correlated noise can be compactly written as a linear functional of the lagged by one sample of the sufficient statistic of the correlated noise and its orthogonal innovations process. Then, we use this structural result to a general low delay quantization problem where we choose to design the encoder and the decoder policies of a multi-input multi-output (MIMO) system with given the past sample of the sufficient statistic of the correlated noise process. For jointly Gaussian processes, we find the minimization problem that needs to be solved, obtain its optimal realization and solve it by showing that is semidefinite representable. Interestingly, the optimal realization of this problem reveals that it suffices only the decoder to have access to the given past sufficient statistic of the correlated noise process but not necessarily the encoder. For scalar-valued processes, we also derive a new analytical expression. The generality of our results (both for vector and scalar processes) is shown by recovering various special cases and known results obtained for independent noise processes.

On semi-infinite systems of convex polynomial inequalities and polynomial optimization problems

We consider the semi-infinite system of polynomial inequalities of the form $$\begin{aligned} {{\mathbf {K}}}:=\{x\in {{\mathbb {R}}}^m\mid p(x,y)\ge 0,\quad \forall y\in S\subseteq {{\mathbb {R}}}^n\}, \end{aligned}$$where p(x, y) is a real polynomial in the variables x and the parameters y, the index set S is a basic semialgebraic set in $${{\mathbb {R}}}^n$$, $$-p(x,y)$$ is convex in x for every $$y\in S$$. We propose a procedure to construct approximate semidefinite representations of $${{\mathbf {K}}}$$. There are two indices to index these approximate semidefinite representations. As two indices increase, these semidefinite representation sets expand and contract, respectively, and can approximate $${{\mathbf {K}}}$$ as closely as possible under some assumptions. In some special cases, we can fix one of the two indices or both. Then, we consider the optimization problem of minimizing a convex polynomial over $${{\mathbf {K}}}$$. We present an SDP relaxation method for this optimization problem by similar strategies used in constructing approximate semidefinite representations of $${{\mathbf {K}}}$$. Under certain assumptions, some approximate minimizers of the optimization problem can also be obtained from the SDP relaxations. In some special cases, we show that the SDP relaxation for the optimization problem is exact and all minimizers can be extracted.

Finding Efficient Solutions for Multicriteria Optimization Problems with SOS-convex Polynomials

In this paper, we focus on the study of finding efficient solutions for a multicriteria optimization problem (MP), where both the objective and constraint functions are SOS-convex polynomials. By using the well-known $\epsilon$-constraint method (a scalarization technique), we substitute the problem (MP) to a class of scalar ones. Then, a zero duality gap result for each scalar problem, its sum of squares polynomial relaxation dual problem, the semidefinite representation of this dual problem, and the dual problem of the semidefinite programming problem, is established, under a suitable regularity condition. Moreover, we prove that an optimal solution of each scalar problem can be found by solving its associated semidefinite programming problem. As a consequence, we show that finding efficient solutions for the problem (MP) is tractable by employing the $\epsilon$-constraint method. A numerical example is also given to illustrate our results.

On the taxicab distance sum function and its applications in discrete tomography

Let a finite set $$F\subset \mathbb {R}^n$$ be given. The taxicab distance sum function is defined as the sum of the taxicab distances from the elements (focuses) of the so-called focal set F. The sublevel sets of the taxicab distance sum function are called generalized conics because the boundary points have the same average taxicab distance from the focuses. In case of a classical conic (ellipse) the focal set contains exactly two different points and the distance taken to be averaged is the Euclidean one. The sublevel sets of the taxicab distance sum function can be considered as its generalizations. We prove some geometric (convexity), algebraic (semidefinite representation) and extremal (the problem of the minimizer) properties of the generalized conics and the taxicab distance sum function. We characterize its minimizer and we give an upper and lower bound for the extremal value. A continuity property of the mapping sending a finite subset F to the taxicab distance sum function is also formulated. Finally we present some applications in discrete tomography. If the rectangular grid determined by the coordinates of the elements in $$F\subset \mathbb {R}^2$$ is given then the number of points in F along the directions parallel to the sides of the grid is a kind of tomographic information. We prove that it is uniquely determined by the function measuring the average taxicab distance from the focal set F and vice versa. Using the method of the least average values we present an algorithm to reconstruct F with a given number of points along the directions parallel to the sides of the grid.

T-optimal designs for multi-factor polynomial regression models via a semidefinite relaxation method

We consider T-optimal experiment design problems for discriminating multi-factor polynomial regression models where the design space is defined by polynomial inequalities and the regression parameters are constrained to given convex sets. Our proposed optimality criterion is formulated as a convex optimization problem with a moment cone constraint. When the regression models have one factor, an exact semidefinite representation of the moment cone constraint can be applied to obtain an equivalent semidefinite program. When there are two or more factors in the models, we apply a moment relaxation technique and approximate the moment cone constraint by a hierarchy of semidefinite-representable outer approximations. When the relaxation hierarchy converges, an optimal discrimination design can be recovered from the optimal moment matrix, and its optimality can be additionally confirmed by an equivalence theorem. The methodology is illustrated with several examples.

Semidefinite Approximations of the Matrix Logarithm

The matrix logarithm, when applied to Hermitian positive definite matrices, is concave with respect to the positive semidefinite order. This operator concavity property leads to numerous concavity and convexity results for other matrix functions, many of which are of importance in quantum information theory. In this paper we show how to approximate the matrix logarithm with functions that preserve operator concavity and can be described using the feasible regions of semidefinite optimization problems of fairly small size. Such approximations allow us to use off-the-shelf semidefinite optimization solvers for convex optimization problems involving the matrix logarithm and related functions, such as the quantum relative entropy. The basic ingredients of our approach apply, beyond the matrix logarithm, to functions that are operator concave and operator monotone. As such, we introduce strategies for constructing semidefinite approximations that we expect will be useful, more generally, for studying the approximation power of functions with small semidefinite representations.

Semidefinite Representation for Convex Hulls of Real Algebraic Curves

We show that the closed convex hull of any one-dimensional semialgebraic subset of $\mathbb{R}^n$ is a spectrahedral shadow, meaning that it can be written as a linear image of the solution set of some linear matrix inequality. This is proved by an application of the moment relaxation method. Given a nonsingular affine real algebraic curve $C$ and a compact semialgebraic subset $K$ of its $\mathbb{R}$-points, the preordering $\mathscr{P}(K)$ of all regular functions on $C$ that are nonnegative on $K$ is known to be finitely generated. Our main result, from which all others are derived, says that $\mathscr{P}(K)$ is stable, meaning that uniform degree bounds exist for weighted sum of squares representations of elements of $\mathscr{P}(K)$. We also extend this last result to the case where $K$ is only virtually compact. The main technical tool for the proof of stability is the archimedean local-global principle. As a consequence of our results we show that every convex semialgebraic subset of $\mathbb{R}^2$ is a spectrahedral shadow.

On minimizing difference of a SOS-convex polynomial and a support function over a SOS-concave matrix polynomial constraint

In this paper, we establish tractable sum of squares characterizations of the containment of a convex set, defined by a SOS-concave matrix inequality, in a non-convex set, defined by difference of a SOS-convex polynomial and a support function, with Slater’s condition. Using our set containment characterization, we derive a zero duality gap result for a DC optimization problem with a SOS-convex polynomial and a support function, its sum of squares polynomial relaxation dual problem, the semidefinite representation of this dual problem, and the dual problem of the semidefinite programs. Also, we present the relations of their solutions. Finally, through a simple numerical example, we illustrate our results. Particularly, in this example we find the optimal solution of the original problem by calculating the optimal solution of its associated semidefinite problem.

Low-Rank Sum-of-Squares Representations on Varieties of Minimal Degree

A celebrated result by Hilbert says that every real nonnegative ternary quartic is a sum of three squares. We show more generally that every nonnegative quadratic form on a real projective variety $X$ of minimal degree is a sum of $\dim(X)+1$ squares of linear forms. This strengthens one direction of a recent result due to Blekherman, Smith, and Velasco. Our upper bound is the best possible, and it implies the existence of low-rank factorizations of positive semidefinite bivariate matrix polynomials and representations of biforms as sums of few squares. We determine the number of equivalence classes of sum-of-squares representations of general quadratic forms on surfaces of minimal degree, generalizing the count for ternary quartics by Powers, Reznick, Scheiderer, and Sottile.

Semidefinite Programming Approach to Gaussian Sequential Rate-Distortion Trade-Offs

Sequential rate-distortion (SRD) theory provides a framework for studying the fundamental trade-off between data-rate and data-quality in real-time communication systems. In this paper, we consider the SRD problem for multi-dimensional time-varying Gauss-Markov processes under mean-square distortion criteria. We first revisit the sensor-estimator separation principle, which asserts that considered SRD problem is equivalent to a joint sensor and estimator design problem in which data-rate of the sensor output is minimized while the estimator's performance satisfies the distortion criteria. We then show that the optimal joint design can be performed by semidefinite programming. A semidefinite representation of the corresponding SRD function is obtained. Implications of the obtained result in the context of zero-delay source coding theory and applications to networked control theory are also discussed.

Semidefinite Representations of Gauge Functions for Structured Low-Rank Matrix Decomposition

This paper presents generalizations of semidefinite programming formulations of 1-norm optimization problems over infinite dictionaries of vectors of complex exponentials, which were recently proposed for superresolution, gridless compressed sensing, and other applications in signal processing. Results related to the generalized Kalman--Yakubovich--Popov lemma in linear system theory provide simple, constructive proofs of the semidefinite representations of the penalty functions used in these applications. The connection leads to several extensions to gauge functions and atomic norms for sets of vectors parameterized via the nullspace of matrix pencils. The techniques are illustrated with examples of low-rank matrix approximation problems arising in spectral estimation and array processing.

On reduced semidefinite programs for second order moment bounds with applications

We show that the complexity of computing the second order moment bound on the expected optimal value of a mixed integer linear program with a random objective coefficient vector is closely related to the complexity of characterizing the convex hull of the points $$\{{1 \atopwithdelims (){\varvec{x}}}{1 \atopwithdelims (){\varvec{x}}}' \ | \ {\varvec{x}} \in {\mathcal {X}}\}$${1x1xź|xźX} where $${\mathcal {X}}$$X is the feasible region. In fact, we can replace the completely positive programming formulation for the moment bound on $${\mathcal {X}}$$X, with an associated semidefinite program, provided we have a linear or a semidefinite representation of this convex hull. As an application of the result, we identify a new polynomial time solvable semidefinite relaxation of the distributionally robust multi-item newsvendor problem by exploiting results from the Boolean quadric polytope. For $${\mathcal {X}}$$X described explicitly by a finite set of points, our formulation leads to a reduction in the size of the semidefinite program. We illustrate the usefulness of the reduced semidefinite programming bounds in estimating the expected range of random variables with two applications arising in random walks and best---worst choice models.

Positive semidefinite rank

Let M be a p-by-q matrix with nonnegative entries. The positive semidefinite rank (psd rank) of M is the smallest integer k for which there exist positive semidefinite matrices $A_i, B_j$ of size $k \times k$ such that $M_{ij} = \text{trace}(A_i B_j)$. The psd rank has many appealing geometric interpretations, including semidefinite representations of polyhedra and information-theoretic applications. In this paper we develop and survey the main mathematical properties of psd rank, including its geometry, relationships with other rank notions, and computational and algorithmic aspects.

Generalized Gauss inequalities via semidefinite programming

A sharp upper bound on the probability of a random vector falling outside a polytope, based solely on the first and second moments of its distribution, can be computed efficiently using semidefinite programming. However, this Chebyshev-type bound tends to be overly conservative since it is determined by a discrete worst-case distribution. In this paper we obtain a less pessimistic Gauss-type bound by imposing the additional requirement that the random vector’s distribution must be unimodal. We prove that this generalized Gauss bound still admits an exact and tractable semidefinite representation. Moreover, we demonstrate that both the Chebyshev and Gauss bounds can be obtained within a unified framework using a generalized notion of unimodality. We also offer new perspectives on the computational solution of generalized moment problems, since we use concepts from Choquet theory instead of traditional duality arguments to derive semidefinite representations for worst-case probability bounds.

Vertices of Spectrahedra arising from the Elliptope, the Theta Body, and Their Relatives

Utilizing dual descriptions of the normal cone of convex optimization problems in conic form, we characterize the vertices of semidefinite representations arising from the Lovasz theta body, generalizations of the elliptope, and related convex sets. Our results generalize vertex characterizations due to Laurent and Poljak in the 1990s. Our approach, focused on the dimension of the normal cone, also leads us to nice characterizations of strict complementarity and to connections with some of the related literature.

Semidefinite Representations of Noncompact Convex Sets

We consider the problem of the semidefinite representation of a class of noncompact basic semialgebraic sets. We introduce the conditions of pointedness and closedness at infinity of a semialgebraic set and show that under these conditions our modified hierarchies of nested theta bodies and Lasserre's relaxations converge to the closure of the convex hull of $S$. Moreover, if the Putinar-Prestel's Bounded Degree Representation (PP-BDR) property is satisfied, our theta body and Lasserre's relaxation are exact when the order is large enough; if the PP-BDR property does not hold, our hierarchies converge uniformly to the closure of the convex hull of $S$ restricted to every fixed ball centered at the origin. We illustrate through a set of examples that the conditions of pointedness and closedness are essential to ensure the convergence. Finally, we provide some strategies to deal with cases where the conditions of pointedness and closedness are violated.