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Jiawang Nie

Publications and source records attributed to Jiawang Nie.

At least 73 records · Page 4Linked to original sources
arXiv2016

Symmetric Tensor Nuclear Norms

This paper studies nuclear norms of symmetric tensors. As recently shown by Friedland and Lim, the nuclear norm of a symmetric tensor can be achieved at a symmetric decomposition. We discuss how to compute symmetric tensor nuclear norms, depending on the tensor order and the ground field. Lasserre relaxations are proposed for the computation. The theoretical properties of the relaxations are studied. For symmetric tensors, we can compute their nuclear norms, as well as the nuclear decompositions. The proposed methods can be extended to nonsymmetric tensors.

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arXiv2016

Positive Maps and Separable Matrices

A linear map between real symmetric matrix spaces is positive if all positive semidefinite matrices are mapped to positive semidefinite ones. A real symmetric matrix is separable if it can be written as a summation of Kronecker products of positive semidefinite matrices. This paper studies how to check if a linear map is positive or not and how to check if a matrix is separable or not. We propose numerical algorithms, based on Lasserre type semidefinite relaxations, for solving such questions. To check the positivity of a linear map, we construct a hierarchy of semidefinite relaxations for minimizing the associated bi-quadratic forms over the unit spheres. We show that the positivity can be detected by solving a finite number of such semidefinite relaxations. To check the separability of a matrix, we construct a hierarchy of semidefinite relaxations. If it is not separable, we can get a mathematical certificate for that; if it is, we can get a decomposition for the separability.

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arXiv2015

Generating Polynomials and Symmetric Tensor Decompositions

This paper studies symmetric tensor decompositions. For symmetric tensors, there exist linear relations of recursive patterns among their entries. Such a relation can be represented by a polynomial, which is called a generating polynomial. The homogenization of a generating polynomial belongs to the apolar ideal of the tensor. A symmetric tensor decomposition can be determined by a set of generating polynomials, which can be represented by a matrix. We call it a generating matrix. Generally, a symmetric tensor decomposition can be determined by a generating matrix satisfying certain conditions. We characterize the sets of such generating matrices and investigate their properties (e.g., the existence, dimensions, nondefectiveness). Using these properties, we propose methods for computing symmetric tensor decompositions. Extensive examples are shown to demonstrate the efficiency of proposed methods.

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arXiv2015

Real Eigenvalues of nonsymmetric tensors

This paper discusses the computation of real Z-eigenvalues and H-eigenvalues of nonsymmetric tensors. A general nonsymmetric tensor has finitely many Z-eigenvalues, while there may be infinitely many ones for special tensors. In the contrast, every nonsymmetric tensor has finitely many H-eigenvalues. We propose Lasserre type semidefinite relaxation methods for computing such eigenvalues. For every nonsymmetric tensor that has finitely many real Z-eigenvalues, we can compute all of them; each of them can be computed by solving a finite sequence of semidefinite relaxations. For every nonsymmetric tensor, we can compute all its real H-eigenvalues; each of them can be computed by solving a finite sequence of semidefinite relaxations. Various examples are demonstrated.

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arXiv2014

Nearly Low Rank Tensors and Their Approximations

The low rank tensor approximation problem (LRTAP) is to find a tensor whose rank is small and that is close to a given one. This paper studies the LRTAP when the tensor to be approximated is close to a low rank one. Both symmetric and nonsymmetric tensors are discussed. We propose a new approach for solving the LRTAP. It consists of three major stages: i) Find a set of linear relations that are approximately satisfied by the tensor; such linear relations can be expressed by polynomials and can be found by solving linear least squares. ii) Compute a set of points that are approximately common zeros of the obtained polynomials; they can be found by computing Schur decompositions. iii) Construct a low rank approximating tensor from the obtained points; this can be done by solving linear least squares. Our main conclusion is that if the given tensor is sufficiently close to a low rank one, then the computed tensor is a good enough low rank approximation. This approach can also be applied to efficiently compute low rank tensor decompositions, especially for large scale tensors.

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arXiv2014

All Real Eigenvalues of Symmetric Tensors

This paper studies how to compute all real eigenvalues of a symmetric tensor. As is well known, the largest or smallest eigenvalue can be found by solving a polynomial optimization problem, while the other middle eigenvalues can not. We propose a new approach for computing all real eigenvalues sequentially, from the largest to the smallest. It uses Jacobian SDP relaxations in polynomial optimization. We show that each eigenvalue can be computed by solving a finite hierarchy of semidefinite relaxations. Numerical experiments are presented to show how to do this.

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arXiv2014

The Hierarchy of Local Minimums in Polynomial Optimization

This paper studies the hierarchy of local minimums of a polynomial in the space. For this purpose, we first compute H-minimums, for which the first and second order optimality conditions are satisfied. To compute each H-minimum, we construct a sequence of semidefinite relaxations, based on optimality conditions. We prove that each constructed sequence has finite convergence, under some generic conditions. A procedure for computing all local minimums is given. When there are equality constraints, we have similar results for computing the hierarchy of critical values and the hierarchy of local minimums. Several extensions are discussed.

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arXiv2014

Free Semidefinite Representation of Matrix Power Functions

Consider the matrix power function X^p defined over the cone of positive definite matrices S^{n}_{++}. It is known that X^p is convex over S^{n}_{++} if p is in [-1,0] or [1,2] and X^p is concave over S^{n}_{++} if p is in [0,1]. We show that the hypograph of X^p admits a free semidefinite representation if p in [0,1] is rational, and the epigraph of X^p admits a free semidefinite representation if p in [-1,0] or [1,2] is rational.

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arXiv2014

The A-truncated K-moment problem

Let A be a finite subset of N^n, and K be a compact semialgebraic set in R^n. An A-tms is a vector y indexed by elements in A. The A-truncated K-moment problem (A-TKMP) studies whether a given A-tms y admits a K-measure or not. This paper proposes a numerical algorithm for solving A-TKMPs. It is based on finding a flat extension of y by solving a hierarchy of semidefinite relaxations {(SDR)_k} for a moment optimization problem, whose objective R is generated in a certain randomized way. If y admits no K-measures and R[x]_A is K-full, then (SDR)_k is infeasible for all K big enough, which gives a certificate for the nonexistence of representing measures. If y admits a K-measure, then for almost all generated R, we prove that: i) we can asymptotically get a flat extension of y by solving the hierarchy {(SDR)_k\}; ii) under a general condition that is almost sufficient and necessary, we can get a flat extension of y by solving (SDR)_k for some k; this occurred in all our numerical experiments; iii) the obtained flat extensions admit a r-atomic K-measure with r <= |A|. The decomposition problems for completely positive matrices and sums of even powers of real linear forms, and the standard truncated K-moment problems, are special cases of A-TKMPs, and hence can be solved numerically by this algorithm.

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arXiv2014

Linear Optimization with Cones of Moments and Nonnegative Polynomials

Let A be a finite subset of N^n and R[x]_A be the space of real polynomials whose monomial powers are from A. Let K be a compact basic semialgebraic set of R^n such that R[x]_A contains a polynomial that is positive on K. Denote by P_A(K) the cone of polynomials in R[x]_A that are nonnegative on K. The dual cone of P_A(K) is R_A(K), the set of all A-truncated moment sequences in R^A that admit representing measures supported in K. Our main results are: i) We study the properties of P_A(K) and R_A(K) (like interiors, closeness, duality, memberships), and construct a convergent hierarchy of semidefinite relaxations for each of them. ii) We propose a semidefinite algorithm for solving linear optimization problems with the cones P_A(K) and R_A(K), and prove its asymptotic and finite convergence; a stopping criterion is also given. iii) We show how to check whether P_A(K) and R_A(K) intersect affine subspaces; if they do, we show to get get a point in the intersections; if they do not, we prove certificates for the non-intersecting.

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arXiv2014

Semidefinite Relaxations for Best Rank-1 Tensor Approximations

This paper studies the problem of finding best rank-1 approximations for both symmetric and nonsymmetric tensors. For symmetric tensors, this is equivalent to optimizing homogeneous polynomials over unit spheres; for nonsymmetric tensors, this is equivalent to optimizing multi-quadratic forms over multi-spheres. We propose semidefinite relaxations, based on sum of squares representations, to solve these polynomial optimization problems. Their properties and structures are studied. In applications, the resulting semidefinite programs are often large scale. The recent Newton-CG augmented Lagrangian method by Zhao, Sun and Toh is suitable for solving these semidefinite relaxations. Extensive numerical experiments are presented to show that this approach is practical in getting best rank-1 approximations.

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arXiv2013

Polynomial Optimization with Real Varieties

We consider the optimization problem of minimizing a polynomial f(x) subject to polynomial constraints h(x)=0, g(x)>=0. Lasserre's hierarchy is a sequence of sum of squares relaxations for finding the global minimum. Let K be the feasible set. We prove the following results: i) If the real variety V_R(h) is finite, then Lasserre's hierarchy has finite convergence, no matter the complex variety V_C(h) is finite or not. This solves an open question in Laurent's survey. ii) If K and V_R(h) have the same vanishing ideal, then the finite convergence of Lasserre's hierarchy is independent of the choice of defining polynomials for the real variety V_R(h). iii) When K is finite, a refined version of Lasserre's hierarchy (using the preordering of g) has finite convergence.

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arXiv2013

Optimality Conditions and Finite Convergence of Lasserre's Hierarchy

Lasserre's hierarchy is a sequence of semidefinite relaxations for solving polynomial optimization problems globally. This paper studies the relationship between optimality conditions in nonlinear programming theory and finite convergence of Lasserre's hierarchy. Our main results are: i) Lasserre's hierarchy has finite convergence when the constraint qualification, strict complementarity and second order sufficiency conditions hold at every global minimizer, under the standard archimedean assumption; the proof uses a result of Marshall on boundary hessian conditions. ii) these optimality conditions are all satisfied at every local minimizer if a finite set of polynomials, which are in the coefficients of input polynomials, do not vanish at the input data (i.e., they hold in a Zariski open set). This implies that Lasserre's hierarchy has finite convergence generically.

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arXiv2012

A Semidefinite Approach for Truncated K-Moment Problems

A truncated moment sequence (tms) of degree d is a vector indexed by monomials whose degree is at most d. Let K be a semialgebraic set.The truncated K-moment problem (TKMP) is: when does a tms y admit a positive Borel measure supported? This paper proposes a semidefinite programming (SDP) approach for solving TKMP. When K is compact, we get the following results: whether a tms y of degree d admits a K-measure or notcan be checked via solving a sequence of SDP problems; when y admits no K-measure, a certificate will be given; when y admits a K-measure, a representing measure for y would be obtained from solving the SDP under some necessary and some sufficient conditions. Moreover, we also propose a practical SDP method for finding flat extensions, which in our numerical experiments always finds a finitely atomic representing measure for a tms when it admits one.

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arXiv2012

Certifying Convergence of Lasserre's Hierarchy via Flat Truncation

This paper studies how to certify the convergence of Lasserre's hierarchy of semidefinite programming relaxations for solving multivariate polynomial optimization. We propose flat truncation as a general 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 general assumptions, and this is also true for the Schmudgen type one; ii) under the archimedean condition, flat truncation is asymptotically satisfied for Putinar type Lasserre's hierarchy, and similar is true for the Schmudgen type one; iii) for the hierarchy of Jacobian SDP relaxations, flat truncation is always satisfied. The case of unconstrained polynomial optimization is also discussed.

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arXiv2011

Regularization Methods for SDP Relaxations in Large Scale Polynomial Optimization

We study how to solve semidefinite programming relaxations for large scale polynomial optimization. When interior-point methods are used, typically only small or moderately large problems could be solved. This paper studies regularization methods for solving polynomial optimization problems. We describe these methods for semidefinite optimization with block structures, and then apply them to solve large scale polynomial optimization problems. The performance is tested on various numerical examples. By regularization methods, significantly bigger problems could be solved on a regular computer, which is almost impossible by interior point methods.

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arXiv2011

Convex Hulls of Quadratically Parameterized Sets With Quadratic Constraints

Let V be a semialgebraic set parameterized by quadratic polynomials over a quadratic set T. This paper studies semidefinite representation of its convex hull by projections of spectrahedra (defined by linear matrix inequalities). When T is defined by a single quadratic constraint, we prove that its convex hull is equal to the first order moment type semidefinite relaxation of $V$, up to taking closures. Similar results hold when every quadratic polynomial is homogeneous and T is defined by two homogeneous quadratic constraints,or V is defined by rational quadratic parameterizations.

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